Staff Software Engineer, Machine Learning, Driver Understanding and Evaluation

About Waymo

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

About the Driver Understanding and Evaluation Team

The Driver Understanding and Evaluation team at Waymo develops a rich understanding of Waymo Driver's behavior. With over 1 million driverless miles per week, it is critical that Waymo can understand and assess the behavior of all its vehicles - both in the field and in simulation - with automated algorithms. The learned metrics team is a strategic bet to use machine learning to ensure we can scale to meet Waymo's goals. We collaborate across teams to bring ML to production systems and build what is Waymo's reward function. We build and operate large-scale machine learning and data systems, simulation workflows, and insight tools. We combine expert human judgements and advanced machine learning models to deliver training and evaluation data for the Waymo driver. We are looking for researchers and software engineers who are passionate about developing production grade machine learning systems for our autonomous vehicles and have an incessant drive to improve the performance of our technology stack.

This role follows a hybrid work schedule and reports to an Engineering Manager.

Responsibilities

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.