Staff Software Engineer (Simulation ML Infrastructure)

• We seek an experienced Senior Machine Learning Infrastructure Engineer to lead the development of advanced AI/ML infrastructure for multi-billion parameter foundation models in ML accelerator-friendly simulations
• Your expertise in massive model scaling, ML accelerators, and distributed training will be required for designing and scaling our systems
• This role reports to an Engineering Manager
• Be part of a world-class, high-performing research engineering team to advance the state of the art of ultra realistic multi-agent simulations using foundation models
• Collaborate closely with the core Google DeepMind and Waymo Realism Modeling teams in London, and Waymo Oxford to use the large models to improve sim realism
• Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models. Work at the intersection of data engineering, model development, and deployment, and provide guidance on architectural decisions and technical directions. Own large, complex systems, driving architectures that meet technical and business objectives
• Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation and model training
• 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
• Mentor junior engineers, growing their expertise and fostering a collaborative culture

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
• Support programs including menopause benefit
• Counseling services
• Second medical opinion for you and your loved ones
• Medical advocacy program for transgender employees
• Competitive compensation
• Regular bonus and equity performance grant opportunities
• Generous 401(k) and regional retirement plans
• 1-on-1 financial coaching
• Annual cross-company compensation review and pay equity analysis
• Fertility and growing family assistance
• Parental leave and baby bonding leave
• Backup childcare
• Elder care and support
• Survivor income benefit
• 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- BS in Computer Science, Robotics, similar technical field of study, or equivalent practical experience
• 5+ years of professional software engineering experience, with at least 3 years in machine learning infrastructure such as developing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model
• MS in Computer Science, Robotics, similar technical field of study, or equivalent practical experience
• 10+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model
• Solid experience in the development and optimization of machine learning infrastructure tools like DeepSpeed, PyTorch, TensorFlow, or similar frameworks
• 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
• Deep understanding of state-of-the-art machine learning models such as auto-regressive transformers and familiarity with custom-kernels for diverse h/w compute based efficiency
• Practical familiarity in Autonomous Driving, Simulations, and ML accelerators is a huge plus