Role Overview
Waymo is hiring a Foundation Model Data, Software Engineer. This is a full-time hybrid role, based in Mountain View, CA, USA; San Francisco, CA, USA; Kirkland, WA, USA; New York City. Part of Waymo's Data Science hiring, posted last week. Full responsibilities, required qualifications, and the apply link are listed in the description below.
Salary Context
Salary is not disclosed in this posting. Market median for Data Science roles is $170k-$236k (based on 150 comparable listings). Many employers share specifics during the interview process or after an initial screen.
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Job description
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 mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.
In this hybrid role, you will report to a Principal Research Scientist.
You will:
- Build and operate the petabyte-scale data systems and ML pipelines at the heart of Waymo's foundation model development
- Shepherd cutting-edge foundation models from research prototypes to robust components within the Waymo Driver
- Develop automated, high-performance infrastructure to ensure the dependable and efficient rollout of data solutions at the forefront of Waymo foundation model program
- Wield large-scale compute and frameworks like Flume/Beam and JAX to process massive datasets for training, evaluating and deploying complex models
- Drive significant leaps in the speed, reliability, and efficiency of the end-to-end ML development lifecycle
- Partner with experts in AI Foundations, ML, and Platform to transform model innovations into tangible improvements to Waymo’s product
You have:
- Undergraduate degree in Computer Science, Machine Learning, Robotics, similar technical field of study, or equivalent practical experience
- Proficiency in Python and C++
- Experience building or maintaining large-scale data pipelines or ML infrastructure (e.g., Flume, Spark, Borg, Kubeflow)
- Strong hands-on SWE skills, able to drive development of large, complex shared codebases
We prefer:
- Experience designing and building distributed systems or MLOps platforms (e.g., model versioning, experiment tracking, CI/CD for ML)
- Familiarity with one of the modern deep learning frameworks (e.g. Pytorch, JAX, Tensorflow)
- Experience in AV planning and related research
- Prior work in an industrial or research setting developing methodologies for data-centric improvement of ML models
- Previous data curation experience and multimodal experience
In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:
- Health, dental, vision, life, disability insurance
- Retirement Benefits: 401(k) with company match
- Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
- Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)
- Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
- Baby Bonding Leave: 18 weeks
- Holidays: 13 paid days per year
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.
About Waymo
Waymo
waymo.com
313 other open roles at Waymo on TryApplyNow.
Frequently Asked Questions
How do I apply for the Foundation Model Data, Software Engineer position at Waymo?
Use the Apply button above to submit your application directly to Waymo. Most applications take less than 5 minutes if your resume and contact details are ready, and you'll be routed to the employer's official application system to finish.
Is the Foundation Model Data, Software Engineer role at Waymo remote or in-office?
This is a hybrid role based in Mountain View, CA, USA; San Francisco, CA, USA; Kirkland, WA, USA; New York City. Expect a mix of in-office and remote days, with the specific cadence set by the hiring manager.
What does a Foundation Model Data, Software Engineer at Waymo earn?
Waymo has not disclosed a salary range in this posting. Many employers share specifics later in the interview process; you can also ask during a recruiter screen if compensation transparency is important to you.
When was the Foundation Model Data, Software Engineer role at Waymo posted?
This role was posted on June 29, 2026 (13 days ago). It's still listed as actively hiring; we re-confirm openings against the source system multiple times per day and remove closed roles.
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