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Waymo

Senior Machine Learning Engineer, Scenes, Traffic Controls

Waymo is hiring a Senior Machine Learning Engineer, Scenes, Traffic Controls in Mountain View, United States. It pays $213k-$263k a year and Level rates it ; you can apply on Waymo.

Level's read on this job

  • Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
  • Pay on this posting starts 7% higher than the typical machine learning engineer posting on Level.
  • The posting mentions PyTorch, TensorFlow and JAX among its tools and skills.
  • Waymo has 367 other open roles on Level.
  • Posted within the past two weeks.

AI in this role

Design, train, and deploy machine learning models for autonomous vehicle scene understanding and traffic control.

pytorchtensorflowjaxcpython
machine-learningcomputer-visionautonomous-drivingmodel-trainingmlops

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.

Driver Refinement and Validation’s (DRV) mission is to build upon the high-quality ML Driver developed by Driver Intelligence, refining its performance to improve autonomous driving in specific, complex real-world contexts and problem areas (e.g., Ride-Hailing contexts). It aims to ensure that the Waymo Driver always computes safe, compliant, comfortable, and useful plans. As a Machine Learning Engineer in DRV, you will bridge the gap between cutting-edge machine learning and real-world vehicle performance, engineering the validation and onboard systems that ensure the autonomous vehicle reliably executes intelligent and predictable maneuvers.

 

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

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

How we rate this

Senior Machine Learning Engineer, Scenes, Traffic Controls at Waymo rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Machine learningComputer visionAutonomous DrivingModel TrainingMLOpsPyTorchTensorFlowJAX

Also common in machine learning engineer jobs at this level

Pay

This job starts at
$213k
Typical machine learning engineer job
$200k

This posting starts 7% above the typical machine learning engineer job. See machine learning engineer jobs

Questions you could be asked

  1. Tell me about a project where machine learning was part of your work. What did you do?
  2. Walk me through a computer vision problem you solved, from raw data to a deployed model.
  3. Tell me about a project where autonomous driving was part of your work. What did you do?
  4. Tell me about a project where model training was part of your work. What did you do?
  5. Tell me about a project where mlops was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Machine learning, Computer vision, Autonomous Driving, Model Training, and MLOps. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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