Staff Tech Lead Manager (Machine Learning, Simulator Evaluation)

• Waymo’s simulator is one of the most complex virtual environments ever built, blending deterministic logic, physical dynamics, and state-of-the-art Generative AI to create a training ground for the Waymo Driver
• Within this cutting-edge ecosystem, the Simulator Evaluation team faces the ultimate data challenge: How do you mathematically prove that a virtual world is “real”?
• While our team programmatically builds the metrics, pipelines, and ML systems that grade this hybrid environment, your challenge as a Staff Tech Lead Manager is to scale both this technology and the highly specialized team executing this vision
• In this role, you will bridge technical vision and execution by translating cutting-edge generative AI advancements into production-ready evaluation tools used by engineering teams across Waymo to ensure the safety and performance of our autonomous fleet, while fostering an inclusive engineering culture of extreme technical rigor, rapid experimentation, and robust software quality
• Lead a top-tier applied ML team focused on building eval frameworks, infrastructure, and AI-powered tooling for World Model understanding and evaluation
• Drive technical direction, and provide technical inputs and guidance to the team
• Architect ML-driven metrics and evaluation frameworks that rigorously test World Model quality, automatically surfacing anomalies and edge cases across vast datasets
• Chart the technical direction for World Model evaluation, working closely with Product and Program managers to translate modeling challenges into clear business objectives
• Build AI-powered internal tools and products that streamline the model eval journey for users, transforming complex model analysis into intuitive, automated, fast-iteration workflows
• Partner with cross-functional teams (e.g., Onboard, Research, and other Simulation teams) and senior technical leadership to deploy your team’s solutions into Waymo’s production-critical systems

Benefits

• Medical, dental, and vision insurance for employees and dependents
• Employee assistance programs focused on mental health
• Personalized workplace adjustments for diverse needs and abilities, including physical, mental, and neurodivergent considerations
• Access to mental health apps
• Onsite wellness centers
• Medical advocacy program for transgender employees
• Counseling services
• Second medical opinion for you and your loved ones
• Support programs including menopause benefit
• Competitive compensation
• Regular bonus and equity performance grant opportunities
• Generous 401(k) and regional retirement plans
• Annual cross-company compensation review and pay equity analysis
• 1-on-1 financial coaching
• Fertility and growing family assistance
• Parental leave and baby bonding leave
• Survivor income benefit
• Elder care and support
• Backup childcare
• Caregiver leave
• Paid time off, including vacation, bereavement, sick leave, parental leave, disability, and holidays
• Jury duty leave
• Military leave
• Hybrid work model with remote work opportunities also available
• Educational reimbursement
• Peer learning and coaching platform
• Donation matching programs
• Volunteer hours
• Employee resource groups
• Internal community groups and local culture clubs
• Inspiring spaces to work, recharge, and collaborate with fellow Waymonauts
• On-site meals and snacks
• Fitness centers, massage programs, and ergonomic support
• On-demand fitness, wellbeing, and cooking classes
• Commuter benefits- 3+ years of hands-on technical leadership experience, directly managing and guiding high-performing engineering teams of 5-10 people
• Strong software engineering proficiency in Python and/or C++
• Proven ability to design robust evaluation frameworks for complex systems, combined with strong analytical skills and proficiency in large-scale data processing
• Experience with ML frameworks like PyTorch, JAX, or Tensorflow, backed by a solid foundation in deep learning, transformers, and large-scale model deployment
• Degree in Computer Science, Robotics, Machine Learning, a related technical field, or equivalent practical experience
• 7+ years of experience building, deploying, and optimizing machine learning systems in production environments
• M.S. or PhD. in Computer Science, Robotics, Machine Learning, or a related quantitative field
• A proven track record of training and/or evaluating large-scale generative models (e.g., reinforcement learning, diffusion, world models, 3D generative models, or video generation)
• Experience training and optimizing models on massive GPU/TPU clusters, and managing large-scale data processing/MLOps systems
• Experience building and deploying AI-powered products or internal developer tools that streamline or automate complex workflows