Staff Machine Learning Engineer, Ad Serving

🏢 Summary: Lead applied research for advertising performance, developing state-of-the-art generative deep-learning models for image and video ads. The role also advances methodologies for conversion modeling, attribution, forecasting, optimization, and experimentation at large scale. 🗂️ Requirements: PhD in Computer Science, Statistics, Applied Mathematics, or related quantitative discipline, 8+ years of applied research experience with statistical and deep-learning techniques, Published research on deep-learning models for advertising or related areas, Strong statistical expertise, Deep experience with state-of-the-art discriminative and generative deep-learning models, Excellent communication and collaboration skills 📃 Skills: Statistics, MachineLearning, DeepLearning, ReinforcementLearning, GenerativeAI, Optimization, Experimentation, Forecasting, Timeseries, Attribution, Calibration, A/BTesting 🏢 Description: Teamwork makes the stream work. Roku is changing how the world watches TV Roku is the #1 TV streaming platform in the U.S., Canada, and Mexico, and we've set our sights on powering every television in the world. Roku pioneered streaming to the TV. Our mission is to be the TV streaming platform that connects the entire TV ecosystem. We connect consumers to the content they love, enable content publishers to build and monetize large audiences, and provide advertisers unique capabilities to engage consumers. From your first day at Roku, you'll make a valuable - and valued - contribution. We're a fast-growing public company where no one is a bystander. We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines. About the team The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers and Roku. The systems and solutions span across different disciplines and technologies to perform realtime multi-objective optimization with distributed systems at large scale and low latencies. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning, Experimentation and Inference Platform that powers the entire landscape which we continuously evolve over time. About the role In this role you will work on applying SOTA research and conduct your own research to develop novel methodologies to solve a large variety of challenging problems in Advertising related to conversion modeling aligned with attribution methodologies/models, calibration, dynamic creative generation and optimization, forecasting and timeseries modeling, yield and margin optimization and Experimentation for A/B and multivariate testing. We’re looking for a strong technical leader with a solid grasp of core statistical techniques and deep experience in SOTA Deep Learning discriminative and generative models. What you’ll be doing Building generative models to generate image and video ads geared towards optimizing performance Stay at the forefront of advancements in related areas We’re excited if you have PhD in a quantitative discipline such as CS, Statistics, Applied Math or a related field 8+ years of experience in applied research using statistical and deep learning techniques Published paper(s) on deep learning models for Advertising or related areas Excellent communication and collaboration skills Preferred Qualifications Experience in the Advertising domain Contributions to open-source ML projects #LI-DH2 What's Roku's approach to hybrid working? Roku fosters an inclusive and collaborative environment where teams generally work in the office Monday through Thursday. Fridays are generally flexible for remote work, except for employees whose specific roles or assigned office location require five days' a week attendance. What are some of the benefits? Roku is committed to offering a diverse range of benefits as part of our compensation package to support our employees and their families. Our comprehensive benefits include global access to mental health and financial wellness support and resources. Local benefits include statutory and voluntary benefits which may include healthcare (medical, dental, and vision), life, accident, disability, commuter, and retirement options (401(k)/pension). Employees are supported in taking time off, in accordance with local leave policies and other personal needs to support their evolving work and life needs. It's important to note that not every benefit is available in all locations or for every role. For details specific to your location, please consult with your recruiter. Accommodations Roku welcomes applicants of all backgrounds and provides reasonable accommodations and adjustments in accordance with applicable law. If you require reasonable accommodation at any point in the hiring process, please direct your inquiries to EmployeeRelations@Roku.com . What should I know about Roku's culture? Roku is a great place for people who want to work in a fast-paced environment where everyone is focused on the company's success rather than their own. We try to surround ourselves with people who are great at their jobs, who are easy to work with, and who keep their egos in check. We appreciate a sense of humor. We believe a fewer number of very talented folks can do more for less cost than a larger number of less talented teams. We're independent thinkers with big ideas who act boldly, move fast and accomplish extraordinary things through collaboration and trust. In short, at Roku you'll be part of a company that's changing how the world watches TV. We have a unique culture that we are proud of. We think of ourselves primarily as problem-solvers, which itself is a two-part idea. We come up with the solution, but the solution isn't real until it is built and delivered to the customer. That penchant for action gives us a pragmatic approach to innovation, one that has served us well since 2002. To learn more about Roku, our global footprint, and how we've grown, visit https://www.weareroku.com/factsheet . By providing your information, you acknowledge that you want Roku to contact you about job roles, that you have read Roku's Applicant Privacy Notice , and understand that Roku will use your information as described in that notice. If you do not wish to receive any communications from Roku regarding this role or similar roles in the future, you may unsubscribe at any time by emailing WorkforcePrivacy@Roku.com .

🏢 Summary: Full Stack Engineer role focused on building an AI-native assistant that delivers reliable, persistent, goal-oriented workflows. The position covers frontend, backend and AI integrations, including agent planning, tool use, LLM reliability and real-time interactions in a remote-first global team. 🗂️ Requirements: Professional full-stack engineering experience across frontend and backend systems, System design, distributed applications and API architecture knowledge, Experience with LLMs, RAG systems or AI-powered applications, Ability to own features from concept through production deployment, Pragmatic decision-making in ambiguous, fast-moving environments, Cross-functional collaboration with engineering, Machine Learning and product teams 📃 Skills: Next.js, Python, Node.js, PyTorch, OpenAI, Anthropic, LLMs, RAG, SQL, NoSQL, Kubernetes, Docker, APIs 🏢 Description: About the product Our client is developing an AI-native assistant designed to support everyday communication, organization, errands and complex workflows with minimal user input. The product must reliably manage long-running processes, retain context, interact with external tools and complete real-world tasks despite the non-deterministic nature of modern AI models. The company’s objective is to make everyday activities significantly faster and easier for users. About the role We are looking for a Full Stack Engineer specializing in AI systems to build the product layer that transforms advanced AI capabilities into useful, production-grade workflows. You will help design how AI agents plan and operate, interact with external tools, manage failures, recover from errors and deliver consistent value to users. This is an end-to-end engineering role covering frontend development, backend systems and AI integrations. What you will do Build end-to-end product functionality across frontend, backend and AI integrations. Design agent workflows covering planning, tool use, multi-step execution, failure handling and recovery. Integrate LLMs, memory systems and external tools into applications that operate reliably under real-world conditions. Design real-time AI interactions involving streaming, partial results and strict latency requirements. Improve system reliability, observability and fallback mechanisms. Collaborate closely with Machine Learning, backend and product teams to deliver functionality from concept to production. Continuously improve the product based on real-world usage, evaluation results and observed failure modes. What we are looking for Strong professional experience in full stack engineering across frontend and backend systems. Solid understanding of system design, distributed applications and API architecture. Experience working with LLMs, RAG systems or other AI-powered applications. Ability to operate effectively in ambiguous situations and make pragmatic engineering decisions. Strong ownership and the ability to take functionality from an initial idea through implementation and production deployment. Confidence working in a fast-moving environment with evolving product requirements. Ability to collaborate effectively across engineering, Machine Learning and product teams. What success looks like AI-native product features progress beyond basic chat interfaces into persistent, goal-oriented workflows. Agent workflows reliably complete multi-step tasks across external tools and multiple user sessions. AI interactions remain responsive and achieve low latency without compromising output quality. Production systems include robust fallback and recovery mechanisms for LLM and external-tool failures. The reliability and completion rate of AI workflows improve through continuous evaluation, monitoring and iteration. Reusable patterns and abstractions support scalable integration of LLMs, memory and external tools. The resulting product experience feels proactive, consistent and dependable to users. Technology stack Next.js Python Node.js PyTorch OpenAI, Anthropic and open-source LLMs SQL and NoSQL databases Kubernetes Docker Compensation and employment The position is offered under an employment contract. The company does not publish a fixed external salary range. Compensation is assessed individually based on: professional experience and technical capability, scope of responsibility, location and relevant market benchmarks, expected impact on the product and organization. Candidates may share their expected compensation at the beginning of the recruitment process. The compensation package consists of a base salary and equity, with flexibility for exceptional candidates. A company laptop will be provided where required for the role. Remote work and global collaboration The company operates as a remote-first, globally distributed organization. There is no fixed company-wide working schedule and no requirement to follow one specific time zone. Team members are expected to maintain sufficient working-hours overlap with their immediate colleagues to collaborate effectively. The successful candidate will work from Poland and collaborate with frontend, backend, Machine Learning and product specialists located across different regions. Poland-based employees join existing global teams rather than a separate local team. The exact reporting line and hiring manager will be confirmed during the recruitment process. How the team works The company believes that outstanding products are built by small, highly capable and hands-on teams. Decisions are made collaboratively, while individuals are expected to take ownership, bring structure to ambiguous problems and execute independently. The team moves quickly while balancing production quality, experimentation and continuous learning from real user behavior. There is no fixed hiring quota for this position. The company is focused on identifying engineers who meet its technical and ownership standards rather than filling a predetermined number of seats. Recruitment process The standard recruitment process consists of up to four stages: Technical assessment, where relevant to the candidate’s background. HR interview. One or more technical interviews. Founder or leadership interview. Particularly strong candidates may be fast-tracked directly to the technical interview stage based on their experience and previous work. Applications are evaluated by members of the technical team. Interviews may be conducted virtually and, where relevant, onsite. The exact format and interviewers may vary. The company aims to make decisions efficiently and provide candidates with a prompt outcome.

🏢 Summary: Full Stack Engineer role focused on building an AI-native assistant that delivers reliable, persistent, goal-oriented workflows. The position covers frontend, backend and AI integrations, including agent planning, external-tool use, LLM reliability and real-time interactions in a remote-first global team. 🗂️ Requirements: Professional full-stack engineering experience, Frontend and backend systems experience, System design expertise, Distributed applications knowledge, API architecture knowledge, LLM, RAG or AI-powered application experience, Production deployment ownership, Cross-functional collaboration with Engineering, Machine Learning and Product teams 📃 Skills: Next.js, Python, Node.js, PyTorch, OpenAI, Anthropic, LLMs, RAG, SQL, NoSQL, Kubernetes, Docker, APIs 🏢 Description: About the product Our client is developing an AI-native assistant designed to support everyday communication, organization, errands and complex workflows with minimal user input. The product must reliably manage long-running processes, retain context, interact with external tools and complete real-world tasks despite the non-deterministic nature of modern AI models. The company’s objective is to make everyday activities significantly faster and easier for users. About the role We are looking for a Full Stack Engineer specializing in AI systems to build the product layer that transforms advanced AI capabilities into useful, production-grade workflows. You will help design how AI agents plan and operate, interact with external tools, manage failures, recover from errors and deliver consistent value to users. This is an end-to-end engineering role covering frontend development, backend systems and AI integrations. What you will do Build end-to-end product functionality across frontend, backend and AI integrations. Design agent workflows covering planning, tool use, multi-step execution, failure handling and recovery. Integrate LLMs, memory systems and external tools into applications that operate reliably under real-world conditions. Design real-time AI interactions involving streaming, partial results and strict latency requirements. Improve system reliability, observability and fallback mechanisms. Collaborate closely with Machine Learning, backend and product teams to deliver functionality from concept to production. Continuously improve the product based on real-world usage, evaluation results and observed failure modes. What we are looking for Strong professional experience in full stack engineering across frontend and backend systems. Solid understanding of system design, distributed applications and API architecture. Experience working with LLMs, RAG systems or other AI-powered applications. Ability to operate effectively in ambiguous situations and make pragmatic engineering decisions. Strong ownership and the ability to take functionality from an initial idea through implementation and production deployment. Confidence working in a fast-moving environment with evolving product requirements. Ability to collaborate effectively across engineering, Machine Learning and product teams. What success looks like AI-native product features progress beyond basic chat interfaces into persistent, goal-oriented workflows. Agent workflows reliably complete multi-step tasks across external tools and multiple user sessions. AI interactions remain responsive and achieve low latency without compromising output quality. Production systems include robust fallback and recovery mechanisms for LLM and external-tool failures. The reliability and completion rate of AI workflows improve through continuous evaluation, monitoring and iteration. Reusable patterns and abstractions support scalable integration of LLMs, memory and external tools. The resulting product experience feels proactive, consistent and dependable to users. Technology stack Next.js Python Node.js PyTorch OpenAI, Anthropic and open-source LLMs SQL and NoSQL databases Kubernetes Docker Compensation and employment The position is offered under an employment contract. The company does not publish a fixed external salary range. Compensation is assessed individually based on: professional experience and technical capability, scope of responsibility, location and relevant market benchmarks, expected impact on the product and organization. Candidates may share their expected compensation at the beginning of the recruitment process. The compensation package consists of a base salary and equity, with flexibility for exceptional candidates. A company laptop will be provided where required for the role. Remote work and global collaboration The company operates as a remote-first, globally distributed organization. There is no fixed company-wide working schedule and no requirement to follow one specific time zone. Team members are expected to maintain sufficient working-hours overlap with their immediate colleagues to collaborate effectively. The successful candidate will work from Poland and collaborate with frontend, backend, Machine Learning and product specialists located across different regions. Poland-based employees join existing global teams rather than a separate local team. The exact reporting line and hiring manager will be confirmed during the recruitment process. How the team works The company believes that outstanding products are built by small, highly capable and hands-on teams. Decisions are made collaboratively, while individuals are expected to take ownership, bring structure to ambiguous problems and execute independently. The team moves quickly while balancing production quality, experimentation and continuous learning from real user behavior. There is no fixed hiring quota for this position. The company is focused on identifying engineers who meet its technical and ownership standards rather than filling a predetermined number of seats. Recruitment process The standard recruitment process consists of up to four stages: Technical assessment, where relevant to the candidate’s background. HR interview. One or more technical interviews. Founder or leadership interview. Particularly strong candidates may be fast-tracked directly to the technical interview stage based on their experience and previous work. Applications are evaluated by members of the technical team. Interviews may be conducted virtually and, where relevant, onsite. The exact format and interviewers may vary. The company aims to make decisions efficiently and provide candidates with a prompt outcome.