Senior ML Engineer; Driver Understanding and Evaluation

Requirements

• PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience
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• 10+ years of hands-on experience in developing and applying Machine Learning models, with a significant focus on Reinforcement Learning
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• 2+ years of people management experience
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• Demonstrated expertise in deep learning, sequence modeling, and generative models
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• Strong publication record or history of impactful project delivery in RL or related areas
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• Proficiency in Python and standard ML frameworks (e.g., JAX, Tensor Flow)
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• Experience with large-scale distributed training and data processing
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• Proven ability to lead complex and ambiguous technical projects from conception to completion
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• (Desirable) 12+ years of relevant experience in ML/RL research and application
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• (Desirable) Experience in the autonomous vehicles domain, robotics, or complex simulation environments
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• (Desirable) Deep understanding of state-of-the-art RL techniques, including those used for fine-tuning large models (e.g., from human feedback/preferences)
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• (Desirable) Familiarity with large-scale simulation platforms and their integration with ML training workflows
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• (Desirable) Experience designing and using metrics for evaluating complex AI systems
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• (Desirable) Track record of technical leadership, influencing senior stakeholders, and driving innovation across team boundaries
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• (Desirable) Excellent communication skills, with the ability to articulate complex technical concepts clearly

• 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
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• 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
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• We are looking for researchers and software engineers who are passionate about developing machine learning techniques
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• These techniques are for the Evaluation systems on our autonomous service
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• They will serve as a constant driver to improve the performance of our technology stack
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• Grow the end-to-end strategy for our next generation of machine learning-based evaluation metrics, promoting scientific and statistical rigor across our embodied AI applications
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• Architect and build scalable systems for training and fine-tuning large-scale generative models to produce realistic and evaluate interesting driving behaviors
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• Lead the design, implementation, and iteration of novel RL algorithms, reward functions, and training paradigms tailored for generating high-fidelity and insightful driving behaviors
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• Lead the development of cutting-edge Deep Learning models and Generative AI (LLM/VLM) solutions. These solutions will enhance human-led triaging, introduce automation for high-volume workflows, and perform nuanced analysis of self-driving behavior to detect critical anomalies
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• Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a novel Reinforcement Learning from Human Preference (RLHF) based data collection and evaluation system
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• Provide technical mentorship, guidance, and thought leadership to other engineers within the team and across collaborating groups
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• Guide and align multiple teams—including Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—on a cohesive evaluation strategy, ensuring cross-functional alignment on goals and priorities