Senior Machine Learning Engineer, Maneuvering Tech
AI in this role
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 an 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:
- Develop next-generation, ML-powered systems that enhance the capabilities of the ML driver and accelerate the rapid scaling of Waymo’s business
- Fine-tune, adapt, and benchmark state-of-the-art generative models to improve autonomous vehicle perception, motion prediction, and trajectory planning
- Write high-quality, scalable, and thoroughly tested code to bring cutting-edge ML into production
- Partner with world-class engineers and product managers to deliver safe and smooth autonomous vehicle behaviors
You have:
- MS in Computer Science, Machine Learning, Robotics, or related technical field, or equivalent practical experience
- 5+ years of industry experience deploying, evaluating, and maintaining ML-based systems in real-world, production environments
- Proficient programming skills in Python and/or C++, coupled with strong analytical and debugging abilities
- Expertise with modern deep learning frameworks, such as JAX or PyTorch
We prefer:
- Experience in deep learning, reinforcement learning, and ML systems
- Domain expertise in solving prediction, behaviors, motion planning, or related robotics problems
- ML post-training experience
- Prior industry experience (e.g. internships) in applied ML or software development
- Publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA, IROS, RSS, CoRL, ACL, or EMNLP)
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 USDHow we rate this
Senior Machine Learning Engineer, Maneuvering Tech at Waymo rates 98 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.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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- What's a project where you used PyTorch hands-on?
- Walk me through how you've used Jax in your day-to-day work.
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
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- List these exact terms on your resume: PyTorch and Jax. 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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