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July 3, 2026
Senior Data Scientist, Systems Performance
Senior • Hybrid
Las Vegas, NV
Mission Summary
The Systems Readiness and Performance team is responsible for driving system design, verifying and validating the autonomy stack, and defining, measuring, and validating system performance targets for fully driverless IONIQ 5 robotaxis in Las Vegas.
Rigorous behavioral and system performance evaluation is critical to scaling the service and achieving long-term goals. The team is seeking a Senior Data Scientist to lead initiatives that improve evaluation and testing methodologies, measure the quality and trustworthiness of the evaluation portfolio, and strengthen the health of the evaluation ecosystem.
In this role, you will lead development of evaluation methodologies and metrics that assess the quality and business relevance of solutions spanning on-road and off-board data.
What You'll Be Doing
- Lead the development of evaluation frameworks for the autonomous system using rigorous, data-driven approaches.
- Collaborate with Functional Safety and Systems Engineering teams to align evaluation metrics with automotive safety standards such as SOTIF and ISO 21448.
- Ensure evaluation metrics are reliable enough to support safety cases and launch readiness decisions.
- Monitor evaluation metrics and incoming performance data for drift, inconsistencies, and degradation.
- Drive performance analysis using statistical methods for simulation and on-road data.
- Develop statistical analysis methods for AV performance data.
- Partner with triage operators and simulation engineers to convert disengagements and edge cases into simulation scenarios.
- Use fleet and evaluation data to identify edge cases and coverage gaps.
- Build confidence in the evaluation framework through data-driven insights and communication with stakeholders.
- Establish correlation between on-road and simulation data.
- Analyze large datasets to solve ambiguous performance questions.
- Establish self-service tooling for developers to evaluate the impact of changes.
- Develop metrics, interpret trends, and investigate anomalies in simulation and on-road data.
- Collaborate with developers to drive actions based on evaluation results.
- Promote data-aware decision making and best practices.
- Mentor engineers and foster collaboration.
- Introduce ML methods for scalable performance evaluation.
What You Bring
- 5+ years of industry experience solving complex problems with large datasets.
- Bachelor’s or higher degree in a quantitative field; Master’s or PhD preferred.
- Strong problem-solving skills.
- Strong Python and SQL skills.
- Experience using data analysis libraries with large datasets.
- Experience applying advanced statistical and ML methods.
- Experience with statistical analysis, hypothesis testing, causal analysis, and data analysis.
- Ability to work independently and drive projects to actionable results.
- Strong communication and interpersonal skills.
- Willingness to learn and teach new statistical and ML techniques.
Bonus Points
- Experience with adversarial scenario generation and closed-loop simulation environments.
- Experience in autonomous driving or robotics.
- Familiarity with data pipelines and distributed compute such as AWS.
- Expertise in Machine Learning and Deep Learning.
- Expertise in sequence modeling and probabilistic ML.
- Familiarity with automotive safety standards such as ISO 26262 or ISO 21448.
Benefits
- Hybrid schedule with optional fully remote work.
- Medical, dental, and vision coverage.
- 401k with company match.
- Health savings accounts.
- Life insurance.
- Pet insurance.
- Bonus and equity opportunities may apply.
- Salary range: $149,000—$198,500 USD.
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What You Bring - 5+ years of industry experience solving complex problems with large datasets. - Bachelor’s or higher degree in Computer Science, Computer Engineering, Data Science, Robotics, Physics, Mathematics, or related quantitative field. - Strong problem-solving skills and logical thinking. - Strong Python and SQL skills with experience using data analysis libraries. - Experience applying advanced statistical and ML methods to large datasets. - Experience with statistical analysis, hypothesis testing, causal analysis, and data analysis. - Ability to work independently and drive projects from definition to actionable results. - Strong communication and interpersonal skills. - Willingness to learn and teach new statistical and ML techniques. 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🏢 Summary: Senior-level ML engineering role focused on defining architecture and technical strategy for large-scale multimodal machine learning systems powering data discovery and model improvement in autonomous driving. The position leads cross-functional initiatives, builds scalable data mining and real-time inference frameworks, and sets engineering standards across the organization. It combines hands-on system design with technical leadership and mentorship. 🗂️ Requirements: BS in Computer Science, Machine Learning or related field (or equivalent experience), 8+ years of hands-on ML engineering experience, Proven ownership of architecture, deployment and optimization of large-scale ML systems, Experience with multimodal foundation models in production (integration, scaling, fine-tuning, deployment), Demonstrated technical leadership across multi-quarter, multi-person initiatives, Expert-level Python proficiency, Strong experience with PyTorch, TensorFlow or JAX, Strong software engineering fundamentals (system design, CI/CD, containerization), Experience deploying ML models in AWS, GCP or Azure, Experience optimizing ML systems for latency, throughput and hardware efficiency, Ability to mentor engineers and drive cross-team technical alignment 📃 Skills: Python, PyTorch, TensorFlow, JAX, AWS, GCP, Azure, CI/CD, Docker, Kubernetes, GPU, CPU, DeepLearning, ActiveLearning, GenerativeAI, MLOps, Triton, TorchServe, TFServing 🏢 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 Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence department-wide technical strategy. By establishing robust processes and mentoring others, you will ensure ML platforms act as a reliable, mission-critical engine for the autonomy stack. What You'll Do: Define Technical Strategy & Roadmaps: - Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems - Inform leadership to guide reprioritization aligned with OKRs and KPIs Architect System-Level Solutions: - Own system-level architecture for complex ML products - Design scalable frameworks for massive data mining and optimized real-time inference across GPU/CPU clusters Drive Cross-Functional Execution: - Lead multi-person projects across teams to completion - Influence partner team roadmaps to solve shared problems and build alignment Elevate Engineering Excellence: - Establish standards for ML system design, code quality, testing and deployment - Deliver processes to proactively address issues and participate in incident response planning Operate as a Generalist Expert: - Apply deep learning, representation learning, active learning and generative AI techniques to complex problems - Unblock teams facing unprecedented technical challenges Mentor and Lead: - Coach senior and junior engineers - Lead architectural reviews and contribute to engineering culture through documentation and tech talks What We're Looking For (Must-Haves): - BS in Computer Science, Machine Learning or related field (or equivalent practical experience) - 8+ years of hands-on ML engineering experience with large-scale ML systems - Experience with multimodal foundation models in production systems (camera, LiDAR, radar, text) - Demonstrated technical leadership across multi-quarter initiatives - Expert-level Python and experience with PyTorch, TensorFlow or JAX - Strong software engineering fundamentals including system design, CI/CD and containerization - Experience deploying ML models in AWS, GCP or Azure and optimizing for latency and throughput - Proven ability to mentor peers and drive consensus across teams Bonus Points: - MS or PhD in Computer Science, Machine Learning or related field - Background in autonomous driving, robotics or real-time decision-making systems - Experience with large-scale ML data mining, active learning loops and representation learning - Familiarity with multimodal learning, sensor fusion or large foundation models - Experience with model serving tools such as TF Serving, Triton or TorchServe - Experience leading severity reviews or establishing incident response processes for ML platforms Work Arrangement: Hybrid schedule with in-office time in Boston, Pittsburgh or Las Vegas, or fully remote. Compensation & Benefits: Salary range: $205,000—$272,500 USD (base salary). Additional compensation may include bonus or company equity. Eligible employees may participate in benefits including medical, dental, vision, 401k with company match, health savings accounts, life insurance and pet insurance.
Technology

Motional
Staff Machine Learning Engineer
Senior
Hybrid
San Francisco, CA
🏢 Summary: Staff Machine Learning Engineer role focused on architecting and leading large-scale ML systems that power multimodal data discovery and model improvement for autonomous driving. The position owns system-level architecture, technical strategy, and cross-functional execution across massive data mining and real-time inference platforms. It combines deep hands-on ML expertise with technical leadership and mentorship at department scale. 🗂️ Requirements: BS in Computer Science, Machine Learning, or related field (or equivalent experience), 8+ years of hands-on ML engineering experience, Proven ownership of architecture, deployment, and optimization of large-scale ML systems, Experience with multimodal foundation models in production (integration, scaling, fine-tuning, deployment), Technical leadership experience defining multi-quarter roadmaps and leading multi-person initiatives, Expert proficiency in Python and at least one major ML framework (PyTorch, TensorFlow, or JAX), Strong software engineering fundamentals (system design, CI/CD, containerization), Broad ML expertise across model training, deep learning, evaluation, and large-scale deployment, Experience deploying ML models in cloud environments (AWS, GCP, or Azure), Ability to mentor engineers and drive cross-team technical alignment 📃 Skills: Python, PyTorch, TensorFlow, JAX, AWS, GCP, Azure, CI/CD, Containerization, DeepLearning, RepresentationLearning, ActiveLearning, GenerativeAI, MultimodalModels, ModelServing, MLOps, GPU, CPU 🏢 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 Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for machine learning systems that power data discovery and model improvement lifecycles. You will architect scalable systems spanning multimodal representation learning, active learning loops, and high-efficiency production inference. You will own system-level architecture, lead multi-quarter initiatives, and partner across engineering to influence department-wide technical strategy while establishing robust processes and mentoring others. What You'll Do: - Define Technical Strategy & Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems, aligning with team and department OKRs and KPIs. - Architect System-Level Solutions: Own architecture for complex ML products and design scalable frameworks for massive data mining and optimized real-time inference across GPU/CPU clusters. - Drive Cross-Functional Execution: Lead multi-person projects across teams and influence partner roadmaps to solve shared technical challenges. - Elevate Engineering Excellence: Establish standards for ML system design, code quality, testing, deployment, and incident response planning. - Operate as a Generalist Expert: Apply deep learning, representation learning, active learning, and generative AI to complex problems and unblock teams facing novel challenges. - Mentor and Lead: Coach engineers, lead architectural reviews, and contribute to engineering culture through documentation and technical talks. What We're Looking For (Must-Haves): - BS in Computer Science, Machine Learning, or related field (or equivalent practical experience) - 8+ years of hands-on ML engineering experience with large-scale ML systems - Experience with multimodal foundation models in production systems (camera, LiDAR, radar, text) - Demonstrated technical leadership across multi-quarter, multi-person initiatives - Expert proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX) - Strong software engineering fundamentals including system design, CI/CD, and containerization - Broad ML experience across training, architectures, evaluation, and large-scale deployment - Experience deploying ML models in cloud environments (AWS, GCP, or Azure) with latency and throughput optimization - Proven mentorship and cross-team collaboration skills Bonus Points (Nice-to-Haves): - MS or PhD in Computer Science, Machine Learning, or related field - Background in autonomous driving, robotics, or real-time decision-making systems - Experience with large-scale ML data mining, active learning loops, and contrastive/representation learning - Familiarity with multimodal learning, sensor fusion, or large foundation models - Experience with model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms - Experience leading severity reviews or incident response planning for mission-critical ML platforms Work Arrangement & Compensation: - Hybrid schedule with in-office collaboration in Boston, Pittsburgh, or Las Vegas, or fully remote - Base salary range: $205,000—$272,500 USD - Eligible for additional compensation such as bonus or company equity - Benefits may include medical, dental, vision, 401k with company match, health savings accounts, life insurance, pet insurance, and more
Technology

Motional
Staff Machine Learning Engineer
Senior
Hybrid
Pittsburgh, PA
🏢 Summary: Staff Machine Learning Engineer role focused on architecting and leading large-scale, production-grade ML systems for multimodal data mining and model improvement in autonomous driving. The position drives technical strategy, system-level design, and deployment of scalable ML frameworks across GPU/CPU clusters. It combines deep ML expertise with technical leadership to build mission-critical platforms for data discovery and real-time inference. 🗂️ Requirements: BS in Computer Science, Machine Learning, or related field, 8+ years of ML engineering experience, Proven ownership of architecture, deployment, and optimization of large-scale ML systems, Experience with multimodal foundation models in production systems, Technical leadership in defining roadmaps and leading multi-person initiatives, Expert proficiency in Python, Strong experience with PyTorch, TensorFlow, or JAX, Strong software engineering fundamentals (system design, CI/CD, containerization), Experience across model training, deep learning, evaluation, and production deployment, Experience deploying ML models in AWS, GCP, or Azure, Experience optimizing ML systems for latency, throughput, and hardware efficiency 📃 Skills: Python, PyTorch, TensorFlow, JAX, AWS, GCP, Azure, CI/CD, Docker, Kubernetes, GPU, CPU, DeepLearning, ActiveLearning, MLOps 🏢 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 Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack. What You'll Do: Define Technical Strategy & Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs. Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters. Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment. Elevate Engineering Excellence: Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning. Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges. Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional's engineering culture through internal documentation, tech talks, and collaborative design. What We're Looking For (Must-Haves): BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience) 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text) Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization) Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams Bonus Points (Nice-to-Haves): MS/PhD in Computer Science, Machine Learning, or a related field. Background in autonomous driving, robotics, or complex real-time decision-making systems. Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning. Familiarity with multimodal learning, sensor fusion, or large foundation models. Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms. Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms. 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$205,000—$272,500 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.