Machine Learning Engineer, Growth

🏢 Summary: Staff Software Engineer role leading the technical strategy and architecture of a scalable Incentive Offers platform, building identity, decisioning, and measurement systems for real-time, high-volume distributed environments. The position focuses on architecting data-intensive, event-driven services that connect physical-world signals with digital member profiles and automated campaigns. You will drive platform evolution, solve complex distributed systems challenges, and shape engineering standards across the organization. 🗂️ Requirements: Advanced degree in Computer Science or related field, 8+ years building customer-facing applications and distributed systems, Expert proficiency in Scala, Java, or JVM-based languages, Experience architecting data-intensive and real-time event-driven systems, Experience leading technical strategy for large engineering initiatives, Experience with AI/ML tools in engineering or system intelligence, Strong experience with high-volume transaction processing and data consistency, Experience designing observability, monitoring, and automated testing solutions 📃 Skills: Scala, Java, TypeScript, React, JVM, MySQL, PostgreSQL, Snowflake, AWS, Git, GitHub, Datadog, AI, ML 🏢 Description: Who we are The real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy — a future where mundane repetition disappears and being known unlocks access, comfort, and belonging everywhere you go. From transforming parking into a seamless drive-in, drive-out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical. The future isn’t coming; it’s here, and we need builders, innovators, and problem solvers to help us create it. Who you are Metropolis is seeking a Staff Software Engineer to lead the technical evolution of our Incentive Offers platform, serving as a critical architect for our identity, decisioning, and measurement infrastructure. You will play a pivotal role in transforming vision-based location signals into a scalable demand engine for our partners by building the platform layer that connects arrivals to automated campaigns. As a technical steward and influencer, you will solve complex technical challenges that few others can while mentoring engineers and setting the direction for project impact and engineering craft across the organization. What you'll do Define the technical strategy and long-term roadmap for the Incentive Offers platform Architect scalable services for offer management, campaign eligibility, and business rule enforcement Design high-throughput infrastructure for real-time member communication and preference management Develop identity resolution systems that connect physical-world vehicle signals with digital member profiles Build robust attribution and measurement engines to track closed-loop redemption across diverse verticals Engineer seamless integrations between our platform and external partner systems or point-of-sale technologies Mentor senior engineering talent and advocate for high standards of engineering craft and efficiency Solve complex distributed systems problems related to high-volume transaction processing and data consistency Partner with Product and Marketing leaders to translate business objectives into scalable technical solutions Identify and resolve systemic bottlenecks to improve developer velocity and system reliability What we're looking for Advanced degree in Computer Science or a related technical field 8+ years of experience building customer-facing applications and complex distributed systems Demonstrate expert proficiency in Scala, Java, or similar JVM-based languages Experience with AI/ML tools to improve engineering productivity or system intelligence Show a proven track record of leading technical strategy for significant engineering initiatives Possess a strong background in architecting data-intensive platforms and real-time event-driven systems Communicate complex technical concepts effectively to both technical and non-technical stakeholders Mentor engineers to foster a high-performance engineering culture and technical growth Design for operational excellence including observability, monitoring, and automated testing Navigate high levels of ambiguity and deliver results in a fast-paced environment While not required, these are a plus: Familiarity with marketing technology stacks or consumer-facing loyalty platforms Experience working in high-growth startup environments or industry-leading tech companies Our Stack Languages + Frameworks: TypeScript, React, Scala (principally), Java (limited) Datastores: MySQL, PostgreSQL, Snowflake Cloud: AWS Version control: Git & GitHub AI Tooling: Copilot on GitHub and Claude Code Observability: Datadog 4 Days in Office: Metropolis values in-person collaboration to drive innovation, strengthen culture, and enhance the Member experience. Our corporate team members hold to our office-first model, which requires employees to be on-site at least four days a week, fostering organic interactions that spark creativity and connection When you join Metropolis, you'll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. The anticipated base salary for this position is $200,000.00 USD to $250,000.00 USD annually. The actual base salary offered is determined by a number of variables, including, as appropriate, the applicant's qualifications for the position, years of relevant experience, distinctive skills, level of education attained, certifications or other professional licenses held, and the location of residence and/or place of employment. Base salary is one component of Metropolis' total compensation package, which may also include access to or eligibility for healthcare benefits, a 401(k) plan, short-term and long-term disability coverage, basic life insurance, a lucrative stock option plan, bonus plans, and more. #LI-CM1 #LI-OnsiteMetropolis may utilize an automated employment decision tool (AEDT) to assess or evaluate your candidacy for employment or promotion. AEDTs are used to assist in assessing a candidate’s application relative to the required job qualifications and responsibilities listed in the job posting. As part of this process, Metropolis retains data relevant to your candidacy, including personal information, for a period that is reasonably necessary for the use of the tool. If you are hired for the position, your data may become part of your employee records. Metropolis Technologies is an equal opportunity employer. We make all hiring decisions based on merit, qualifications, and business needs, without regard to race, color, religion, sex (including gender identity, sexual orientation, or pregnancy), national origin, disability, veteran status, or any other protected characteristic under federal, state, or local law.

🏢 Summary: Staff Software Engineer role focused on owning and evolving billing, payments, and invoicing systems for a high-scale revenue platform processing tens of millions in monthly transactions. The position drives technical architecture, payment integrations, financial correctness, and compliance while partnering with finance and operations teams. You will design resilient distributed revenue systems that ensure transaction integrity, scalability, and auditability. 🗂️ Requirements: 8+ years engineering experience building revenue or payment systems, Proven design of distributed financial systems handling edge cases and transaction integrity, Strong expertise in databases and financial data management, Production experience integrating payment gateways (e.g., Stripe, PayPal), Experience with compliance standards such as PCI-DSS, Ability to design audit logging, reconciliation, and financial reporting systems, Advanced proficiency in Java and JVM internals, Experience using AI-assisted development tools in production codebases, Must be local to Seattle area 📃 Skills: Java, Scala, TypeScript, React, MySQL, PostgreSQL, Snowflake, AWS, Stripe, PayPal, PCI-DSS, Git, GitHub, Datadog, Copilot, Claude, JVM 🏢 Description: Who we are Metropolis is an artificial intelligence company that uses computer vision technology to enable frictionless, checkout-free experiences in the real world. Today, we are reimagining parking to enable millions of consumers to just "drive in and drive out." We envision a future where people transact in the real world with a speed, ease, and convenience that is unparalleled, even online. Tomorrow, we will power checkout-free experiences anywhere you go to make the everyday experiences of living, working, and playing remarkable - giving us back our most valuable asset, time. Who you are Metropolis is seeking a Staff Software Engineer to join our Revenue Platform team. In this role, you will own the technical direction for our billing, payments, and invoicing infrastructure, charting the course for a system that already processes tens of millions in monthly transactions. You will balance immediate business needs with long-term architectural improvements, identifying high-impact enhancements and designing practical migration strategies to support our rapid growth. As a technical leader, you will partner closely with engineering, finance, and operations teams to solve complex challenges involving pricing rules, subscription billing, and enterprise invoicing. You are a systems thinker who enjoys understanding how things work and building the roadmaps that improve a platform while it runs. Your work will directly shape how Metropolis monetizes its platform and expands into new verticals beyond parking, ensuring our financial systems remain resilient, compliant, and remarkably efficient. What you’ll do Own the technical architecture and evolution of Metropolis's billing, payments, and invoicing systems, establishing standards for financial accuracy, transaction integrity, and compliance that ensure every dollar is correctly captured and accounted for Design and implement resilient payment processing with support for multiple payment gateways (Stripe, etc.), intelligent routing between processors, retry strategies for failed transactions, and graceful handling of partial failures to maximize payment success rates Build comprehensive monitoring and reporting for revenue operations, including payment success rates, billing accuracy metrics, reconciliation dashboards, failed transaction alerts, and audit trails that provide visibility from customer transactions to accounting close Partner with finance and operations teams on payment failures, billing disputes, refund workflows, and reconciliation issues – building tools and automation that reduce manual intervention and improve mean time to resolution for customer-impacting revenue issues Drive adoption of revenue system best practices across services, including idempotent payment processing, distributed transaction handling, compensation workflows for failures, audit logging, and testing strategies that ensure financial correctness Own integrations with external systems, including payment gateways, ERP, data analytics, etc., ensuring reliable data flow and maintaining compliance requirements across the revenue stack What we’re looking for 8+ years of hands-on engineering experience with a focus on building revenue systems like payment platforms Strong systems thinking for financial correctness with proven ability to design distributed revenue systems that handle edge cases gracefully: partial failures, duplicate requests, timing issues, currency conversion, tax calculation, and maintaining transaction integrity across service boundaries Database and financial data expertise, including transaction isolation, audit logging, point-in-time recovery, handling financial data migrations, and experience with systems that require both high availability and absolute correctness Payment gateway and integration experience with production knowledge of Stripe, PayPal, or similar processors; tax calculation services; accounting system integrations; and the operational reality of depending on external financial services, including compliance requirements like PCI-DSS Excellent technical communication with the ability to influence technical decisions across teams (particularly finance, accounting, and data teams), document complex systems, conduct post-mortems, and establish reliability standards organization-wide Experience with sophisticated usage of rapidly developing AI-assisted development skills in real-world codebases, including fluent use of Claude Code/GPT Codex/Windsurf or similar tools in multi-agent mode, spec-driven development (SDD), MCP/skills optimization, context/prompt engineering Local to the Seattle area While not required, these are a plus BS or MS in Computer Science or a relevant engineering discipline Expert-level revenue systems engineering with hands-on experience building billing platforms, payment processing pipelines, reconciliation systems, and handling challenges like idempotent transactions, failed payment retries, proration logic, multi-currency support, and revenue recognition – you've seen these problems before and know the patterns that work Expert revenue systems engineering with hands-on experience building billing platforms, payment processing, reconciliation, and managing complex challenges like idempotent transactions, payment retries, proration, multi-currency support, and revenue recognition. Financial systems expertise with deep experience implementing audit trails, reconciliation workflows, revenue reporting, and compliance controls at scale – you understand what it takes to maintain penny-perfect accuracy Java expertise with a deep understanding of JVM performance, concurrency, and operational characteristics. Scala experience is a big plus Experience with AI-powered development tools such as Claude Code, GitHub Copilot, or similar agentic coding tools for enhanced productivity Our Stack Languages + Frameworks: TypeScript, React, Scala (principally), Java (limited) Datastores: MySQL, PostgreSQL, Snowflake Cloud: AWS Version control: Git & GitHub AI Tooling: Copilot on GitHub and Claude Code Observability: Datadog 4 Days in Office: Metropolis values in-person collaboration to drive innovation, strengthen culture, and enhance the Member experience. Our corporate team members hold to our office-first model, which requires employees to be on-site at least four days a week, fostering organic interactions that spark creativity and connection. When you join Metropolis, you'll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. The anticipated base salary for this position is $220,000 USD to $250,000 USD annually. The actual base pay offered is determined by several variables, including, as appropriate, the applicant's qualifications for the position, years of relevant experience, distinctive skills, level of education attained, certifications or other professional licenses held, and the location of residence and/or place of employment. Base pay is one component of Metropolis's total compensation package, which may also include access to or eligibility for healthcare benefits, a 401(k) plan, short-term and long-term disability coverage, basic life insurance, a lucrative stock option plan, bonus plans, and more.Metropolis may utilize an automated employment decision tool (AEDT) to assess or evaluate your candidacy for employment or promotion. AEDTs are used to assist in assessing a candidate’s application relative to the required job qualifications and responsibilities listed in the job posting. As part of this process, Metropolis retains data relevant to your candidacy, including personal information, for a period that is reasonably necessary for the use of the tool. If you are hired for the position, your data may become part of your employee records. Metropolis Technologies is an equal opportunity employer. We make all hiring decisions based on merit, qualifications, and business needs, without regard to race, color, religion, sex (including gender identity, sexual orientation, or pregnancy), national origin, disability, veteran status, or any other protected characteristic under federal, state, or local law.

🏢 Summary: The offer is for a Machine Learning Systems Engineer focused on optimizing large-scale distributed training systems for frontier models. The role centers on performance profiling, GPU kernel development, and improving throughput, scalability, and reliability of ML training pipelines. It directly supports accelerating model convergence through high-performance systems engineering. 🗂️ Requirements: Bachelor’s, Master’s or PhD in Computer Science, Computer Engineering or related field, Strong proficiency in Python, Extensive hands-on experience with PyTorch, Experience with distributed training using PyTorch Distributed, Experience optimizing ML model training and inference performance, Experience with GPU kernel development in CUDA or Triton, Ability to perform performance profiling and bottleneck analysis, Strong understanding of machine learning concepts and architectures 📃 Skills: Python, PyTorch, CUDA, Triton, Nsight, PyTorchDistributed, GPU, KernelFusion, Sharding, Tiling 🏢 Description: Mission Summary:We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at scale, focusing obsessively on speed, cost, reliability, and throughput. You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and reduce the time-to-convergence for our next generation of models. What you'll be doing: Performance Profiling & Optimization: Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time. Distributed Training: Optimize distributed training pipelines using frameworks such as PyTorch Distributed. Kernel Development: Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads. Data Pipeline Engineering: Optimize robust data loading pipelines that maximize training throughput. What we're looking for: Education: Bachelor's, Master's degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline. Software Engineering: Strong proficiency in Python. ML Frameworks: Extensive hands-on experience with PyTorch. ML Knowledge: Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes. Problem Solving: Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges. We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process. Candidates for certain positions are eligible to participate in Motional's benefits program. Motional's benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more.Salary Range$144,000—$192,000 USDMotional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We're driven by something more. Our journey is always people first. We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move. Higher purpose, greater impact. We're creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it's not only good for our business, it's the right thing to do. Scale up, not starting up. Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We're driven to scale; we're moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges. Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit www.Motional.com and follow us on Twitter, LinkedIn, Instagram and YouTube. Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibility.

🏢 Summary: Machine Learning Engineer role focused on building and scaling ML-powered data mining pipelines for multimodal autonomous driving data, leveraging foundation models to discover rare edge cases and improve model performance. The position involves training and deploying models, developing embedding-based search and active learning workflows, and supporting production monitoring. You will collaborate across teams to translate ML prototypes into scalable, production-ready systems. 🗂️ Requirements: BS or MS in Computer Science, Machine Learning, or related field, Hands-on experience with PyTorch, TensorFlow, or JAX, Strong proficiency in Python, Experience with SQL and large-scale datasets, Proficiency with Pandas and NumPy, Knowledge of version control and unit testing, Understanding of full ML lifecycle from data preparation to deployment 📃 Skills: Python, PyTorch, TensorFlow, JAX, SQL, Pandas, NumPy, LiDAR, CI/CD, Git 🏢 Description: Mission Summary:At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine. You will work with state-of-the-art foundation models to extract insights from Motional's driving data, working at the intersection of large-scale representation learning and data retrieval. By building smarter mining tools and efficient data pipelines, you will accelerate the model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation. What You'll Do: Build and Train ML Pipelines: Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). Focus on implementing supervised and self-supervised learning approaches to improve data search and retrieval. Support Model Deployment: Implement scalable data preprocessing and augmentation pipelines. Assist in applying standard optimization techniques (e.g., batch inference, quantization) to ensure models run efficiently in production environments. Data Mining & Analysis: Help develop embedding-based search tools and "active learning" workflows to identify critical driving scenarios. Monitor Production Performance: Help build and maintain dashboards to monitor model health, data drift, and system performance. Identify regressions and assist in the operational support of our data mining services. Learn and Apply Best Practices: Follow software engineering standards (version control, CI/CD, unit testing) for ML code. Participate in code reviews and contribute to technical documentation. Collaborate Across Teams: Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable, scalable engineering solutions. What We're Looking For (Must-Haves): BS or MS in Computer Science, Machine Learning, or a related field. Hands-on experience with PyTorch (preferred) or TensorFlow/JAX. You should be comfortable training models and evaluating them using standard metrics. Strong proficiency in Python with the ability to write clean, modular, and well-documented code. Working knowledge of version control, unit testing, and basic software design patterns. Experience working with large datasets, including proficiency in SQL and data libraries like Pandas and NumPy. A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation and deployment basics. A proactive learner who thrives on constructive feedback and is eager to grow within a high-stakes engineering environment. Bonus Points (Nice-to-Haves): MS/PhD in Computer Science, Machine Learning, or related field. Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning. Background in autonomous driving, robotics, or real-time decision-making systems. Familiarity with multimodal learning, sensor fusion, or embodied AI. Experience building active learning loops, using the model to find the data that breaks the model. Experience with ML-based data mining, active learning, or contrastive learning. Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms. Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV) We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process. Candidates for certain positions are eligible to participate in Motional's benefits program. Motional's benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more.Salary Range$144,000—$192,000 USDMotional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We're driven by something more. Our journey is always people first. We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move. Higher purpose, greater impact. We're creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it's not only good for our business, it's the right thing to do. Scale up, not starting up. Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We're driven to scale; we're moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges. Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit www.Motional.com and follow us on Twitter, LinkedIn, Instagram and YouTube. Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibility.

🏢 Summary: Machine Learning Engineer role focused on designing, building, and deploying production ML systems across Lyft Business, including pricing, fraud detection, marketplace optimization, and agentic AI applications. The position involves owning models end-to-end, developing scalable ML infrastructure, and translating business problems into impactful ML solutions. The role operates in high-scale environments influencing revenue, operations, and user experience. 🗂️ Requirements: Experience with GenAI and LLM ecosystems, Experience building and deploying production ML models, Experience with pricing, marketplace, or fraud ML problems, Experience with feature engineering and ML pipelines, Experience with cloud ML services, Ability to productionize research prototypes, Experience running experiments and evaluating ML performance, Ability to design scalable ML systems, Proficiency in writing production-quality code 📃 Skills: Python, MachineLearning, LLM, GenAI, RAG, LangChain, LangGraph, SageMaker, Bedrock, AWS, GraphNN, KnowledgeGraphs, NetworkAnalysis, APIs, Experimentation 🏢 Description: At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Machine Learning is at the heart of Lyft’s products and decision-making. Machine Learning Engineers at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges, from pricing and marketplace frameworks that ensure reliability and competitiveness, to agentic AI platforms that automate analytical workflows, to behavioral detection systems that protect the integrity of our network. We operate at the intersection of applied ML and real business impact, shipping models that directly influence revenue, rider experience, and partner trust. Lyft Business builds products that help organizations move the people who matter most—employees, customers, patients, and guests—easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs. We're looking for a Machine Learning Engineer to design, build, and deploy ML systems across Lyft Business. This is a high-scope role: you won't be siloed into one problem area. Instead, you'll move across pricing algorithms, fraud and behavior detection, agentic AI systems, and emerging ML applications as the business evolves. You'll write production-quality code, own models end-to-end from prototyping through deployment, and collaborate closely with Data Scientists, Product Managers, and Software Engineers to translate complex business problems into scalable ML solutions. This role is ideal for someone who is technically versatile, energized by variety, and wants to see their work directly shape a large-scale business. Responsibilities: Develop and deploy ML models across multiple problem domains — including dynamic pricing, marketplace optimization, fraud detection, and anomaly/behavior detection — in production environments serving millions of rides Build and iterate on agentic AI systems (e.g., LLM-powered analytical agents) that automate decision-making and reduce operational overhead Design and implement feature pipelines, model training workflows, and serving infrastructure using Lyft's ML platform Partner with Data Scientists on the Algorithms and Decisions teams to take research prototypes from proof-of-concept to production at scale Evaluate ML system performance against business KPIs, run experiments, and drive continuous model improvement Identify new opportunities where ML can create leverage across Lyft Business verticals (Healthcare, Lyft Pass, Business Travel) and pitch solutions Contribute to team engineering standards — code quality, observability, documentation, and testing practices Experience: Experience with GenAI / LLM ecosystems — prompt engineering, RAG, agent frameworks (e.g., LangChain, LangGraph), or fine-tuning Exposure to graph-based ML methods (graph neural networks, knowledge graphs, network analysis) Experience with pricing, marketplace, or fraud-related ML problems Familiarity with cloud ML services (AWS SageMaker, Bedrock) or internal ML platforms Track record of identifying and scoping ML projects independently, not just executing on pre-defined specs Benefits: Great medical, dental, and vision insurance options with additional programs available when enrolled Mental health benefits Family building benefits Child care and pet benefits 401(k) plan with company match to help save for your future In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible Subsidized commuter benefits Monthly Lyft credits and complimentary Lyft Pink membership Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law. Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid The expected base pay range for this position in the San Francisco area is $176,000-$211,200, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

🏢 Summary: Senior Machine Learning Engineer role focused on developing and deploying deep learning solutions for prediction, planning, and control within an autonomous driving platform. The position involves leading experiments, building large-scale production ML systems, and collaborating with cross-functional teams to deliver industrial-grade models. It requires extensive experience in autonomous vehicles or robotics and deep expertise in modern ML frameworks and large language models. 🗂️ Requirements: BS/MS/PhD in Computer Science, Electrical Engineering, Mechanical Engineering, Applied Math or related field, 15+ years of experience building ML systems for autonomous vehicles or robotics, Deep understanding of large language models and transformers, Hands-on experience building and deploying large-scale production ML systems, Experience with DNN training, inference and optimization, Strong mathematical foundation in machine learning and deep learning, Proven leadership and innovation track record 📃 Skills: Python, MachineLearning, DeepLearning, LLM, Transformers, PyTorch, TensorFlow, TensorRT, DNN, Robotics, AutonomousVehicles 🏢 Description: What you'll be doing: Researching, implementing, and evaluating deep-learning-based methods for prediction and planning for Autonomous Vehicle products. Leading, designing, running, and analyzing experiments and testing to evaluate the efficiency of solutions on real-world data. Partnering with system software engineering specialists to ship industrial strength ML models. Communicating and collaborating with multi-functional teams. What We're Looking For: BS/MS/PhD in computer science, electrical engineering, mechanical engineering, applied math, or related fields (or equivalent experience) 15+ years of proven experience building ML systems for autonomous vehicles or similar robotics applications Deep understanding of large language models (LLM) and transformers Hands-on experience building large scale production ML systems and deploying them at scale Excellent leadership and track record for innovation to help build the next generation of ML-based planning solutions Experience with deep neural network (DNN) training, inference and optimization in leading frameworks (Pytorch, Tensorflow, TensorRT, etc.) Excellent understanding of the mathematical foundations of machine learning and deep learning Bonus Points: Prior experience as a ML planning lead Proven publication record in ML for planning, vision, or related fields Prior experience building and deploying vision language action or chain of thought models Benefits: Salary range: $271,000—$373,000 USD (base salary) Eligibility to participate in benefits program including medical, dental, vision, 401k with company match, health savings accounts, life insurance, pet insurance, and more.

🏢 Summary: Senior Machine Learning Engineer role focused on building and scaling end-to-end recommendation systems for personalized content and workout experiences. The position involves developing real-time ML services, running experiments, and deploying production-grade models in a cloud-based environment. You will contribute to unified recommendation architecture and LLM-driven personalization initiatives. 🗂️ Requirements: 4+ years of experience in machine learning, Experience in recommender systems or applied ML domains, Degree in Computer Science, Machine Learning, Statistics, Mathematics or related quantitative field, Hands-on experience building, training and evaluating ML models, Strong software engineering skills, Professional experience with Python, Experience with large-scale data pipelines, Experience with distributed data processing, Experience with Spark and Airflow, Experience with AWS or similar cloud environment, Experience with Kubernetes and production serving technologies, Experience with A/B testing and experiment analysis 📃 Skills: Python, Spark, Airflow, AWS, S3, Kubernetes, RecommenderSystems, DeepLearning, Transformers, LLMs, MLOps, REST, gRPC, FastAPI, ABTesting 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Senior Machine Learning Engineer to join the Personalization team, which owns the recommendation systems powering content discovery across the ecosystem. In this role, you will work on end-to-end ML systems: building and training models, improving real-time recommendation services, running experiments, and deploying scalable solutions into production. You will help evolve the recommendation platform from multiple models toward a more unified, real-time architecture, while also contributing to company's IQ initiatives involving LLMs, personalized plans, and AI-powered member experiences. Key takeaways: Stack : Python, Spark, Airflow, AWS, S3, Kubernetes, recommender systems, Deep Learning, Transformers, LLMs, MLOps, REST/gRPC/FastAPI, A/B testing Salary : 31 000 PLN - 35 000 PLN gross on the Contract of Employment Working model : Hybrid - 3 days/week from the office Location : ul. Grzybowska 60, Warsaw Recruitment process : A call with MOTIFE Recruiter Hiring Manager screening Coding interview Panel interviews with the team (coding, architecture, and cross-collaboration interviews, Hiring Manager meeting) Responsibilities: Build, train, evaluate, and improve ML models powering personalized workout and content recommendations. Develop and maintain data and ML pipelines using Python, Spark, Airflow, AWS, and S3. Productionize, deploy, monitor, and optimize ML models and recommendation services. Improve real-time model serving, including latency, scalability, reliability, and infrastructure costs. Support the evolution of recommendation systems toward unified rankers, candidate generation, and inference services. Run A/B tests and analyze experiment results with Product Analysts to measure product and member impact. Contribute to IQ initiatives, including personalized plans, insights, LLM-based features, and responsible AI practices. Collaborate with ML Engineers, Data Engineers, Software Engineers, Platform Engineers, Product Managers, and Analysts to deliver scalable personalization features. Requirements: 4+ years of experience in machine learning, ideally in recommender systems, NLP, computer vision, or another applied ML domain. Degree in Computer Science, Machine Learning, Statistics, Mathematics, Operational Research, or another quantitative field. Strong hands-on experience building, training, evaluating, and improving ML models. Solid software engineering skills, including clean code, data structures, algorithms, and production readiness. Professional experience with Python; experience with Java, Kotlin, Go, C, or C++ is a plus. Experience with large-scale data pipelines, distributed processing, and orchestration tools such as Spark and Airflow. Familiarity with cloud-based ML environments, preferably AWS, and production serving technologies such as Kubernetes, REST, gRPC, or FastAPI. Strong communication skills and the ability to work cross-functionally with product, analytics, platform, data, and engineering teams. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If this sounds like your next step, we’d love to hear from you! Please apply via our careers page and submit your CV in English.

🏢 Summary: Senior Machine Learning Engineer role focused on building and deploying distilled multimodal teacher-student models and reinforcement learning systems to power large-scale data mining for autonomous driving. The position involves designing knowledge distillation pipelines, optimizing real-time inference, and developing RL-based reasoning and agentic systems for production environments. The work directly accelerates model improvement, data discovery, and large-scale ML deployment across multimodal sensor data. 🗂️ Requirements: BS in Computer Science, Machine Learning, or related field or equivalent experience, 6+ years of machine learning engineering experience focused on model post-training, optimization, and deployment, Strong experience with model distillation and teacher-student training frameworks, Proven experience with reinforcement learning including policy optimization and reward design, Expert-level proficiency in Python, Experience with PyTorch, TensorFlow, or JAX, Strong software engineering fundamentals including testing, CI/CD, containerization, and system design, Experience deploying ML models in cloud environments (AWS, GCP, or Azure), Experience optimizing models for real-time inference in production environments 📃 Skills: Python, PyTorch, TensorFlow, JAX, PPO, DQN, Actor-Critic, AWS, GCP, Azure, CI/CD, Docker, Kubernetes, TF-Serving, Triton, TorchServe 🏢 Description: Mission Summary: At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, powers this discovery. As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine by designing massive multimodal Teacher models and distilling them into hyper-efficient Student models capable of searching exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search and apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the model improvement lifecycle for post-training analysis, error diagnosis, and dataset curation. What You'll Do: Architect and Train Distilled Models: - Design and implement teacher-student model frameworks for multimodal sensor data - Develop training pipelines for knowledge distillation - Ensure student models maintain high accuracy while reducing inference latency and memory footprint Reinforcement Learning for Data Discover: - Build RL-based policy learning and reasoning systems for autonomous driving applications - Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction - Explore reward shaping, environment modeling, and multi-agent RL where applicable Optimize Model Deployment for Real-Time Inference: - Collaborate to deploy distilled and RL models into production - Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters - Implement model versioning, A/B testing, and monitoring for performance regressions Research and Integrate Agentic Systems: - Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior - Integrate such systems into the broader autonomy stack as experimental or production components Drive Production Reliability: - Establish patterns for graceful degradation, fault tolerance, and cost optimization - Operate Omnitag as a mission-critical data platform with focus on reliability and debuggability Mentor and Collaborate: - Work with ML scientists, data engineers, and autonomy teams to translate research into scalable engineering solutions - Guide junior engineers in model training, evaluation, and deployment best practices What We're Looking For: - BS in Computer Science, Machine Learning, or related field, or equivalent professional experience - 6+ years of hands-on experience in machine learning engineering with focus on model post training, optimization, and deployment - Strong experience with model distillation or teacher-student training including loss functions and evaluation of compressed models - Proven experience with reinforcement learning including policy optimization, reward design, simulation environments, and RL-based reasoning - Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX) - Strong software engineering fundamentals: testing, CI/CD, containerization, and system design - Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference - Demonstrated ability to ship production-grade ML systems and mentor team members Bonus Points (Nice-to-Haves): - MS/PhD in Computer Science, Machine Learning, or related field - Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning - Background in autonomous driving, robotics, or real-time decision-making systems - Familiarity with multimodal learning, sensor fusion, or embodied AI - Experience building active learning loops - Experience with ML-based data mining, active learning, or contrastive learning - Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms - Publications or open-source contributions in RL, distillation, or efficient ML We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. The salary range for this role is an estimate based on a wide range of compensation factors including specific skills, experience, role location, certifications, licenses, and business needs. The estimated compensation range reflects base salary only and may include additional compensation such as bonus or company equity. Candidates for certain positions are eligible to participate in benefits including medical, dental, vision, 401k with company match, health saving accounts, life insurance, pet insurance, and more. Salary Range $172,000—$229,000 USD

🏢 Summary: The offer is for a Machine Learning Engineer responsible for developing, evaluating, and deploying predictive ML models focused on Ad Score and Ad Account Health. The role involves end-to-end model development, feature engineering, data analysis, and integration of ML solutions into production systems. It requires close collaboration with cross-functional teams and applying advanced ML techniques to marketing and advertising data. 🗂️ Requirements: Master’s or Ph.D. in related field, Proven experience developing and deploying predictive ML models, Strong expertise in regression, classification, clustering, deep learning, Proficiency in feature engineering and model evaluation, Strong programming skills in Python, Experience with ML libraries such as TensorFlow, PyTorch, or scikit-learn, Experience with SQL, Experience with cloud platforms (GCP, AWS, or Azure), Experience with data warehouses (BigQuery, Snowflake, or Redshift), Experience deploying models via APIs, Experience with Git/GitHub 📃 Skills: Python, SQL, TensorFlow, PyTorch, scikit-learn, GCP, AWS, Azure, BigQuery, Snowflake, Redshift, Git, GitHub, APIs, MachineLearning, DeepLearning, Regression, Classification, Clustering 🏢 Description: About the Role: We are in search of an exceptional Machine Learning Engineer to join our accomplished team. In this role, you will take the lead in developing and fine-tuning predictive ML models, with a primary focus on Ad Score and Ad Account Health. You will play a crucial part in delivering actionable insights and solutions to our clients, and your work will be integral to our mission. Responsibilities include but are not limited to; ML Model Development: Lead the development and refinement of predictive ML models, particularly Ad Score and Ad Account Health. Data Analysis: Conduct in-depth data analysis to identify trends, patterns, and insights that inform model development and optimization. Feature Engineering: Collaborate with data engineers to create and maintain feature engineering pipelines to support model training. Model Evaluation: Implement rigorous evaluation methodologies to assess model performance, making necessary adjustments for continuous improvement. Deployment and Integration: Work closely with engineering teams to deploy models and integrate them into our products through APIs. Collaboration: Collaborate closely with product managers, full-stack engineers, and TPMs to ensure seamless integration of data science solutions into our products. Research and Innovation: Stay up-to-date with the latest developments in the field of data science and machine learning, and explore innovative approaches to problem-solving. Requirements Master's or Ph.D. in a related field with a strong academic background. Proven experience as a Data Scientist with a track record of developing and deploying predictive ML models. Expertise in machine learning techniques, including but not limited to regression, classification, clustering, and deep learning. Proficiency in data manipulation, feature engineering, and model evaluation. Strong programming skills in languages such as Python and experience with libraries like TensorFlow, PyTorch, or scikit-learn. Excellent communication skills and the ability to collaborate effectively within cross-functional teams. A passion for continuous learning and staying updated with the latest trends and technologies in data science. Strong problem-solving abilities and the capacity to translate complex data into actionable insights. Required knowledge of: Python SQL Cloud Platforms (GCP, AWS, Azure) Data Warehouses (BigQuery, Snowflake, Redshift) LLMs / AI APIs Git / GitHub Nice to have: Data Transformation (dbt) Semantic Layers (Cube, Looker, dbt Metrics) TypeScript Bayesian modeling experience - ideally Marketing Mix Models (PyMC, Stan, or similar..). Understands priors, MCMC sampling, posterior diagnostics. Causal inference / experimentation- geo experiments (matched markets), A/B testing at scale. Familiar with incrementality measurement. Marketing/advertising domain- understanding of attribution, media channels (paid social, search, display, video), campaign structures. Nice to have - familiarity with adstock/saturation curves and budget optimization Our interview process includes, but is not limited to the following: Excel and Typing Test We offer a competitive salary and benefits based on ability level, including: Unlimited vacation policy Monthly Phone Stipend Comprehensive Medical, Dental, and Vision insurance options 401(K) plan with matching Dog friendly office Hybrid work opportunity Professional Development Program Bonus Perk - Seamless allowance Total compensation based on education, experience, and skills level ($90,900-$254,100) Level 1 - Possesses essential capabilities $90,900-$123,540 Level 2 - Possesses developing capabilities $123,540-$156,180 Level 3 - Possesses notable capabilities. $156,180-$188,820 Level 4 - Possesses strong capabilities. $188,820-$221,460 Level 5 - Possesses advanced capabilities. $221,460-$254,100 About WITHIN WITHIN is the world's first Performance Branding company, partnering with some of the biggest brands in the world to drive business growth through innovative marketing strategies. Our integrated operating model collapses the traditional marketing silos between creative and media, performance and brand, and across media channels. With a full suite of offerings including media, creative, SEO, Lifecycle, Retail Media, Affiliate and Influencer, we're able to work with our brand partners in an integrated fashion, allowing us to align marketing strategies back to core business objectives. Client teams at WITHIN are trained on how to always act as a trusted business partner, acting as a fiduciary to client needs above our own. Teams at WITHIN have the ability to work with iconic brands such as The North Face, Timberland, Ben and Jerry's and Jose Cuervo. Everyone at WITHIN wants to grow and be challenged. It's a collaborative place made up of small, closely knit and versatile teams that are fast and adaptive to solve problems and build systems. Check out some of our work! We weave AI into everything we do, using the latest tech across all teams to innovate, work smarter, and make better decisions. Whether it's in creative, operations, or anything else, AI helps us level up and do things at a whole new scale. We expect our people to use AI in their daily work, fully embracing it as a critical tool to help us succeed. Join Our Network! Stay connected with us and be the first to know about new opportunities, industry insights, and updates. Follow us on: LinkedIn WhatsApp Community Instagram Tik Tok Locations New York City: 43-01 22nd St, Suite 602, Queens, NY 11101, United States Bogotá: WeWork Av. Carrera 19 #100-45 Usaquén, Piso (Floor) 10, Bogotá, Distrito Capital de Bogotá 110111, Colombia Mexico City: Av. Insurgentes Sur 1082, Piso (Floor) 2, Oficina 2008, Ciudad de México, CDMX 03100, México AI-Assisted Screening Notice As part of our initial application review process, we may use an AI-assisted tool to help compare skills and job titles from your resume with the requirements of the role. This tool evaluates information based on contextual relevance and is used only to support our manual review process. It is not used to make hiring decisions. If you are a resident of New York City and would like to request an alternative evaluation process or a reasonable accommodation, please contact us at legal@within.co.

🏢 Summary: Senior Machine Learning Engineer role focused on building and optimizing multimodal teacher-student models and reinforcement learning systems to power large-scale data mining for autonomous driving. The position involves designing distilled models, scaling RL workflows, and deploying production-grade ML systems optimized for real-time inference. The work directly supports intelligent data discovery, model improvement, and reliable operation of a mission-critical ML platform. 🗂️ Requirements: BS in Computer Science, Machine Learning or related field or equivalent experience, 6+ years of hands-on machine learning engineering experience, Experience with model distillation or teacher-student training frameworks, Experience with reinforcement learning including policy optimization and reward design, Expert proficiency in Python, Proficiency with PyTorch, TensorFlow or JAX, Strong software engineering fundamentals including testing and CI/CD, Experience with containerization, Experience deploying ML models in cloud environments (AWS, GCP or Azure), Experience optimizing models for production inference 📃 Skills: Python, PyTorch, TensorFlow, JAX, PPO, DQN, AWS, GCP, Azure, CI/CD, Docker, Kubernetes, TFServing, Triton, TorchServe 🏢 Description: Mission Summary: At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery. As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation. What You'll Do: Architect and Train Distilled Models: - Design and implement teacher-student model frameworks for multimodal sensor data. - Develop training pipelines for knowledge distillation. - Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint. Reinforcement Learning for Data Discover: - Build RL-based policy learning and reasoning systems for autonomous driving applications. - Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. - Explore reward shaping, environment modeling, and multi-agent RL where applicable. Optimize Model Deployment for Real-Time Inference: - Collaborate with backend engineers to deploy distilled and RL models into production. - Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. - Implement model versioning, A/B testing, and monitoring for performance regressions. Research and Integrate Agentic Systems: - Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. - Integrate such systems into our broader autonomy stack as experimental or production components. Drive Production Reliability: - Establish patterns for graceful degradation, fault tolerance, and cost optimization. - Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence. Mentor and Collaborate: - Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. - Guide junior engineers in best practices for model training, evaluation, and deployment. What We're Looking For: - BS in Computer Science, Machine Learning, or related field, or equivalent professional experience. - 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment. - Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models. - Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning. - Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX). - Strong software engineering fundamentals: testing, CI/CD, containerization, and system design. - Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference. - Demonstrated ability to ship production-grade ML systems and mentor team members. - Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers. Bonus Points (Nice-to-Haves): - MS/PhD in Computer Science, Machine Learning, or related field. - Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning. - Background in autonomous driving, robotics, or real-time decision-making systems. - Familiarity with multimodal learning, sensor fusion, or embodied AI. - Experience building active learning loops, using the model to find the data that breaks the model. - Experience with ML-based data mining, active learning, or contrastive learning. - Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms. - Publications or open-source contributions in RL, distillation, or efficient ML. We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. Candidates for certain positions are eligible to participate in benefits including medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. Salary Range $172,000—$229,000 USD