Senior Machine Learning Engineer, Scenes, Traffic Controls

You will

• Own ML features for Scene Understanding (traffic controls, signs, special regions such as RRX and flood zones, lane states) end to end: data -> model -> eval -> onboard launch
• Mine and curate large-scale driving data (real and simulated) to find long-tail failures, build training and eval sets, and design labeling and supervision
• Design, train, and iterate on model tasks and heads in large multi-task models. Improve calibration, robustness, and generalization to new geographies
• Build rigorous evaluation: PRT metrics, slicing taxonomies, side-by-sides, and staging launches, and turn the results into launch decisions
• Integrate models into the onboard stack (C++), combining learned outputs with safe heuristic fallbacks, Mendel-guarded rollouts, and map-contradiction handling
• Triage real driving events, find the root cause anywhere in the stack, and propose surgical or systemic fixes. Work closely with Perception, Reasoning, Mapping, and ML Infrastructure teams

You have

• BS/MS/PhD in CS, EE, Robotics, ML, or a related field, or equivalent practical experience.
• Hands-on experience shipping ML models to production, including training, offline evaluation, and deployment (L4: 2+ yrs; L5: 4+ yrs, or a PhD plus 2+ yrs)
• Strong data mining / data engineering: building large-scale batch pipelines (Beam/Flume/Spark or similar) and analytical SQL to find, slice, and curate data
• Proficiency in Python with a modern DL framework (JAX, TensorFlow, or PyTorch) and production C++
• Demonstrated cross-stack generalist work: has owned problems that crossed data, model, and systems/serving boundaries
• Rigorous evaluation instincts: metric design, error analysis and slicing, and A/B or staged rollout

We prefer

• AV/Robotics experience, especially perception (detection, classification, tracking)
• Experience with transformer or temporal models (GRU/RNN), multi-task learning, and model calibration/uncertainty
• Labeling-pipeline and supervision design; autolabeling or synthetic data
• TPU/accelerator performance work; exporting models for onboard/edge inference
• Builds internal tools (visualization, triage, experiment tracking) that accelerate the team

Pay and benefits

The expected base salary range for this full-time position across US locations 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. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.

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 $213,000 - $263,000 USD