Staff Machine Learning Engineer

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.

The DUE ML Core London 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 researchers and software engineers 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 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.
  
  

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
• Strong coding experience in C++ and/or Python.
• Experience in at least one of: Foundational Models, VLM, Deep Learning
• 5+ 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
  
  

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.
  
  

The expected base salary range for this full-time position is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.

Salary Range

£150,000—£162,000 GBP