Role Description As Machine Learning Technical Lead, you own the execution layer of intelligence. You will translate research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product. You will be responsible for making models trainable, deployable, observable, and performant under real-world constraints. This position is 100% Remote. Responsibilities - Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. - Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. - Architect and operate scalable inference systems, balancing latency, cost, and reliability. - Design and maintain data systems for high-quality synthetic and real-world training data. - Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. - Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. - Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. - Make pragmatic trade-offs and ship improvements quickly, learning from real usage. - Work under real production constraints: latency, cost, reliability, and safety. Outcomes - Research and models reliably translate into production-ready solutions with clear performance and quality targets. - ML pipelines, training loops, and inference systems are stable, efficient, and maintainable. - Production issues are detected, debugged, and resolved quickly, minimizing user impact. - Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction. - Iterations on models and systems are measurable, safe, and improve user experience over time. Qualifications - Experience building or shipping real Machine Learning systems used by people, not just demos. - Artificial Intelligence (AI) experience required. - Experience working with large models and understanding their failure modes. - Experience writing strong, production-grade code. - You are self-directed, pragmatic, and take full ownership of outcomes. - You communicate clearly and collaborate well in small, high-trust teams. - Tech Stack: GPU-based training and inference system, JAX, Python, and PyTorch. Benefits - Medical insurance - Dental - Vision - Savings Plan Options - PTO Compensation USD 190,000 - USD 225,000 yearly

Role Description As the Senior Machine Learning Engineer, you are an independent owner of critical Machine Learning (ML) subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale. This is a hands-on, high-impact role focused on depth. This position is 100% Remote. Responsibilities - Build core Machine Learning (ML) systems that power a proactive, long-horizon Artificial Intelligence (AI) product. - Own work end-to-end: data preparation, training, evaluation, inference, and iteration. - Turn research ideas into working systems that run reliably in production. - Debug model failures and system issues using real production signals. - Iterate quickly: ship, measure outcomes, refine, and repeat. - Collaborate closely with research, product, and engineering to deliver real user impact. - Mentor and review work from other Machine Learning (ML) engineers through example and technical judgment. - Work under real production constraints: latency, cost, reliability, and safety. Outcomes - Machine Learning (ML) models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets. - Complex production issues are monitored, debugged, and resolved with minimal disruption. - Training, inference, and data pipelines are robust, scalable, and maintainable over time. - Drive measurable improvements in Machine Learning (ML) systems based on real-world signals and user feedback. - Provide mentorship and technical guidance to peers, raising the overall ML engineering standard. - Collaborate cross-functionally to ensure Machine Learning (ML) features integrate seamlessly into products and meet business goals. Qualifications - Experience building and shipping Machine Learning (ML) systems used by real users. - Artificial Intelligence (AI) experience required. - Experience understanding how modern Machine Learning (ML) models behave and misbehave in production. - Experience writing strong, production-quality code and think in systems, not scripts. - Experience taking ownership, work independently, and push work across the finish line. - You learn fast, communicate clearly, and improve through iteration. - Tech Stack: GPU-based training and inference systems, JAX, Python, and PyTorch. Benefits - Medical insurance - Dental - Vision - Savings Plan Options - PTO Compensation USD 170,000 - USD 190,000 yearly

Role Description The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgments and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack. - Build scalable systems for training and fine-tuning large-scale models to evaluate interesting driving behaviors. - Work at the intersection of data engineering, model development, and simulation. - Provide guidance on architectural decisions and technical directions. - Own large, complex systems, driving architectures that meet technical and business objectives. - Contribute to the production and optimization of machine learning models aiming to assess Waymo’s expansive fleet of vehicles that cumulatively travel millions of miles. - Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation, model training, and evaluation. - Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. - Translate product/business goals into measurable technical deliverables, ensuring system component alignment. Qualifications - M.S. or Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience. - 5+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model. - A history of contributions to machine learning tooling and frameworks e.g. PyTorch, Jax, Tensorflow, Ray, or similar. - Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks. Requirements - 7+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model. - Experience in the autonomous vehicles domain, robotics, or complex simulation environments. - Familiarity with large-scale simulation platforms and their integration with ML training workflows. Benefits - Waymo employees are eligible to participate in Waymo’s discretionary annual bonus program. - Equity incentive plan. - Generous Company benefits program, subject to eligibility requirements. Salary Range The expected base salary range for this full-time position across US locations is $213,000 — $263,000 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level.

Role Description As the Staff Machine Learning Engineer, you own the execution layer of intelligence. You translate research direction into reliable, scalable, production-grade Machine Learning (ML) systems. This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints. This position is 100% Remote. Responsibilities - Own end-to-end Machine Learning (ML) system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. - Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. - Architect and operate scalable inference systems, balancing latency, cost, and reliability. - Design and maintain data systems for high-quality synthetic and real-world training data. - Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. - Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. - Collaborate closely with application engineering to integrate Machine Learning (ML) systems cleanly into backend, mobile, and desktop products. - Make pragmatic trade-offs and ship improvements quickly, learning from real usage. - Work under real production constraints: latency, cost, reliability, and safety. Outcomes - Research and models reliably translate into production-ready solutions with clear performance and quality targets. - Machine Learning (ML) pipelines, training loops, and inference systems are stable, efficient, and maintainable. - Production issues are detected, debugged, and resolved quickly, minimizing user impact. - Team members are supported, aligned, and able to deliver high-impact Machine Learning (ML) work with minimal friction. - Iterations on models and systems are measurable, safe, and improve user experience over time. Qualifications - Experience building or shipping real Machine Learning (ML) systems used by people, not just demos. - Artificial Intelligence (AI) experience required. - Experience working with large models and understanding their failure modes. - Experience writing strong, production-grade code. - You are self-directed, pragmatic, and take full ownership of outcomes. - Experience communicating clearly and collaborating well in small, high-trust teams. - Tech Stack: GPU-based training and inference system, JAX, Python, and PyTorch. Benefits - Medical insurance - Dental - Vision - Savings Plan Options - PTO Compensation USD 150,000 - USD 170,000 yearly