Jobs at Waymo

Leads industrialization and supplier manufacturing launches for autonomous vehicle sensors and ECUs. Oversees automated assembly-line deployment, process engineering, quality tools, supplier performance, equipment qualification, PPAP, and start of production. Manages technical contracts, capital equipment, schedules, risks, and cross-functional issue resolution. Drives continuous improvement in yield, cycle time, and product quality while coordinating internal engineering, quality, procurement, legal, and external manufacturing partners.

Build and deploy machine learning models for Waymo’s marketplace, including conversion, wait-time, retention, demand forecasting, pricing, matching, and vehicle positioning models. Apply reinforcement learning, design and evaluate experiments, collaborate with Engineering and Product, and present data-driven recommendations to leadership.

Leads Waymo’s enterprise learning, leadership development, organizational capability, and culture strategy. The role establishes scalable learning roadmaps, leadership pipelines, onboarding, skills architecture, measurement practices, ERG support, and governance. It partners with senior leaders and People teams, manages vendors and learning technology, builds an inclusive learning culture, develops high-performing teams, and aligns talent initiatives with business objectives in a fast-growing technology environment.

Research and implement machine learning perception solutions for autonomous driving in weather and degraded-road conditions. Develop models for data-scarce scenarios, evaluate ML systems, and collaborate with onboard software, planning, and control teams. The role involves implementing algorithms from academic papers and potentially working with computer vision, computational geometry, real-time robotics, and vision-language models.

Lead efficiency efforts across Waymo’s Simulator software stack, establishing architecture improvements, performance frameworks, and multi-quarter technical roadmaps. Optimize simulation throughput, latency, and resource utilization across CPU, GPU, and TPU infrastructure. Partner with evaluation and infrastructure teams to resolve performance bottlenecks, develop large-scale evaluation and debugging processes, and guide cross-functional engineering initiatives.

Lead software engineering for autonomous vehicle pick-up and drop-off behaviors. Design cross-component solutions, build quantitative on-road evaluation methods, develop foundational frameworks, analyze large datasets, collaborate with product and onboard teams, mentor engineers, and address challenging long-tail driving scenarios. The role requires substantial complex-systems engineering and management experience, with preferred expertise in autonomous vehicles, robotics, and production machine learning.

Join Waymo's Simulator Team to develop ultra-realistic sensor data and perception simulation for autonomous vehicles using advanced ML technologies. Collaborate with foundational AI research teams and drive innovation in simulation realism.

Manage Operations financial planning processes, forecasts, variance analysis, financial models, and business reviews. Partner with Operations, Product, Data Science, and Accounting teams to assess investments, optimize resources, measure ROI, improve forecasting accuracy, and drive accountability. Translate complex data into strategic insights and influence leadership decisions regarding long-term operational investments and growth strategies.

Counsel, IP will build and manage Waymo’s global patent portfolio across autonomous driving technologies. Responsibilities include patent harvesting, prosecution, portfolio analysis, IP counseling, transactional IP terms, public disclosure review, confidentiality protection, licensing and acquisition evaluation, and management of outside patent counsel. The role advises engineering, product, research, business, supplier, partner, and academic teams while supporting strategic intellectual property objectives.

Conduct sensor data analysis, design and execute hardware validation experiments, develop performance metrics, improve operational processes, and collaborate with cross-functional teams. The role focuses on automotive LiDAR and perception hardware, requiring experimental design, system prototyping, performance evaluation, and technical scripting with Python and SQL. Preferred experience includes signal processing, algorithm design, and sensing systems such as LiDAR, radar, and cameras.

Leads the multi-year vision, strategy, and roadmap for robotic depot automation, including autonomous vehicle charging, cleaning, and inspection. Defines requirements across autonomous driving, fleet orchestration, and robotics hardware; aligns senior leaders; guides prototype-to-production deployments; develops rollout plans; and shapes vehicle platforms and depot infrastructure for zero-touch operations.

Develop scalable infrastructure, simulation, evaluation, and analysis systems that accelerate onboard software development. Responsibilities include optimizing CPU, GPU, TPU, memory, and latency usage in cloud datacenters; evolving autonomous driving simulation architecture; resolving performance bottlenecks; and building evaluation, debugging, and software change-management processes.

Develop and optimize onboard autonomous-driving software for strict latency, reliability, and compute-resource targets. Improve modular architecture, CPU/GPU/TPU utilization, simulation performance, and collision-avoidance reaction times. Collaborate with onboard and hardware teams on performance bottlenecks and hardware/software codesign. Build performance evaluation, debugging, and change-management processes for large-scale vehicle and cloud systems.

Lead Waymo’s People Strategy portfolio, including go-to-market planning, program launches, change management, AI adoption, OKRs, and product portfolio decisions. Build scalable People infrastructure, drive cross-functional initiatives from launch through adoption, assess program effectiveness, and advise senior leaders. The role requires independent judgment, strong relationship management, and discretion when handling sensitive topics. It is hybrid in Mountain View or San Francisco, with travel up to 25%.

Leads Waymo’s People Strategy priorities, including international expansion, market-entry compliance, entity setup, staffing models, workforce planning, and location strategy. Partners with Finance, business leaders, and cross-functional teams to develop headcount models, clarify ambiguous growth challenges, and deliver recommendations to senior executives. Requires independent judgment, discretion, executive influence, and management of multiple high-impact programs across geographies.

Develop and improve reinforcement learning infrastructure for Waymo’s autonomous driving machine learning systems. The intern will support LPM model training, profile and improve training efficiency, integrate new reinforcement learning technologies, and collaborate on large-scale ML infrastructure. Candidates should be pursuing a master’s or PhD in a relevant technical field and have knowledge of machine learning, deep learning, reinforcement learning, modern ML frameworks, and Python software development.

Lead a data science team evaluating autonomous driving compliance with road rules. Define rigorous measurement frameworks, improve statistical evaluation methods, assess performance across territories and hardware platforms, and inform deployment decisions. Partner with product, engineering, and systems teams on roadmaps and milestones. Contribute technically while managing and mentoring data scientists, solving ambiguous problems, and communicating findings to senior stakeholders.

Develop production-grade machine learning models and infrastructure to evaluate autonomous vehicle behavior in simulation and real-world environments. Collaborate across teams to deploy state-of-the-art ML systems, improve model quality, and support large-scale data and simulation workflows.

Lead complex, cross-functional technical programs integrating Waymo’s autonomous service with third-party platforms. Own partner integrations from planning through launch and post-launch quality, define deliverables and schedules, manage risks and dependencies, drive architecture and technical trade-off decisions, and build scalable onboarding frameworks. Collaborate with internal teams, external engineering partners, and executives to deliver consumer-facing products across markets.

Conduct experiments on large-scale generative and foundation model training recipes, analyze evaluation metrics to identify bottlenecks and optimization opportunities, and collaborate with researchers and engineers to integrate techniques into production training pipelines. The internship focuses on deep learning, transformer architectures, generative modeling, and autonomous driving applications.