Staff Machine Learning Engineer (Driver Understanding and Evaluation)

• The DUE ML Core 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 judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver
• Lead the development of cutting edge Deep Learning and machine learning models to enhance human-led triaging and introduce automation for high-volume workflows
• Design and build Gen AI LLM/VLM solutions for self driving car behavior analysis and anomaly detection
• Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a Reinforcement Learning from human preference-based data collection and evaluation system
• Enhance User Feedback Analysis, collaborate seamlessly with product and business teams to design and implement tools for multi-label classifications, sentiment assessment, comment summarization, root cause analysis and trend analysis of rider feedback
• Oversee the production and optimization of machine learning models aiming to assess Waymo’s expansive fleet of vehicles that cumulatively travel millions of miles
• Drive technical direction, and provide technical inputs and guidance to the team
• Work closely with PMs and TPMs to help define product requirements and align the technical agenda with the company’s business objectives
• Collaborate closely with multiple teams (e.g., Prediction, Planning, Research), other technical leads, and senior leaderships across Waymo to deliver on key strategic efforts

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
• Second medical opinion for you and your loved ones
• Counseling services
• 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
• Elder care and support
• Backup childcare
• 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
• Employee resource groups
• Volunteer hours
• 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- Experience in at least one of: Foundational Models, VLM, Deep Learning
• B.S. in Computer Science, Robotics, Machine Learning, similar technical field of study, or equivalent practical experience
• Strong coding experience in C++ and/or Python
• 7+ years of experience with hands-on experience in machine learning projects
• Experience with ML frameworks such as TensorFlow, PyTorch, Hugging Face’s transformers, along with expertise in deep learning models and ML deployment at scale
• M.S. or Ph.D. degree Computer Science or related quantitative field with a specialization of machine learning
• Deep learning experience with Transformers
• 10+ years of experience with hands-on experience in machine learning projects
• Gen AI LLM/VLM experience
• Large-scale data processing and analytical skills
• Experience with building tools for applied machine learning, including MLOps, evaluation/validation techniques, and model performance optimization