Technical Lead Manager Machine Learning, DUE

Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you're an AI Engineer who's curious and passionate about Level 4 autonomous driving, we'd like to meet you.

The Driver Understanding and Evaluation (DUE) team at Waymo is developing rich metrics for understanding the behavior of the Waymo Driver in the real world, and technologies such as context and scene analysis to understand driving, understanding and augmenting real world driving data to generate rare driving events, build large scale data infrastructure, improve components such as agents and a realistic simulator. These technologies come together to drive the overall technical strategy and methodology used to evaluate the behavior of the Waymo Driver.

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. 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. We are looking for software engineers and researchers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack.

In this role, you will

• Lead a top-tier applied ML team focused on building ML models for AV behavior understanding and evaluation, using deep learning and Gen AI.
• 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
• 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.

At a minimum we would like you to have:

• B.S. in Computer Science, Robotics, Machine Learning, similar technical field of study, or equivalent practical experience
• 5+ years of experience leading engineering teams of 5-15 people
• 7+ years of experience with hands-on experience in machine learning projects
• 7+ years of hands-on experience building and deploying machine learning products in production environments
• Experience in at least one of: Foundational Models, VLM, Deep Learning
• Strong coding experience in C++ and/or Python.
• Experience with ML frameworks such as TensorFlow, PyTorch, Hugging Face's transformers, along with expertise in deep learning models and ML deployment at scale

It's preferred if you have:

• M.S. or Ph.D. degree Computer Science or related quantitative field with a specialization of machine learning.
• Deep learning experience with Transformers
• Gen AI LLM/VLM experience
• Experience with building tools for applied machine learning, including MLOps, evaluation/validation techniques, and model performance optimization.
• Large-scale data processing and analytical skills.