Senior Software Engineer, Training Efficiency
Company websitewaymo.com (Waymo, opens in new tab)
Base salary $238K to $302K
Employer-published annual base salary, USD. Advertised base range in Mountain View, CA
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationMountain View, CA
- WorkplaceWorkplace not stated
- Status at last checkSource accepting applications when checked
- Last checked (3 h ago) · observed since
About this job
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 Waymo ML Infrastructure team works with Research and Production teams to develop models in Perception and Planning that are core to our autonomous driving software. We help our partners by offering the best solutions for the entire model development lifecycle. These solutions are developed in close collaboration with teams at Google. They are geared towards both scaling models and solving problems unique to ML for autonomous driving. You will improve the runtime efficiency of input data pipelines for large-scale training workloads. This is a unique opportunity to work on ML systems and improve on our model training processes.
You Will:
- Design, and improve distributed input data pipelines for large-scale ML training workloads.
- Collaborate with researchers and ML engineers to resolve bottlenecks in data pipeline performance.
- Improve runtime goodput of ML training workload, including optimizing input data processing systems, ensuring scalability and reliability across distributed environments.
- Implement and maintain advanced ML infrastructure tools, including ML Pathways, Grain, JAX, and TensorFlow.
- Evaluate and integrate modern technologies to enhance the performance and scalability of ML systems.
- Promote best practices for distributed systems architecture and contribute to technical leadership within the team.
You Have:
- B.S. in Computer Science, Math, or 5+ years equivalent real-world experience.
- Proficient in distributed systems design with an understanding of ML data pipeline optimization.
- Experience with ML frameworks, including TensorFlow and JAX.
- Hands-on experience libraries like Grain or tf.data service.
- Solid programming skills in Python and C++.
- Practical familiarity with profiling tools to uncover performance bottlenecks.
We Prefer:
- MS in Computer Science, Math
- Familiarity with distributed dataflow frameworks like ML Pathways
#LI-Hybrid
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
$238,000—$302,000 USD
Source: Waymo on Greenhouse · View original posting (opens in new tab)
Pay in context
This role's base floor ($238,000) is above 87% of the 4,268 Software Engineering roles in California listed on this site; 6 others list the same floor. Their median floor is $176,100; the middle half runs from $149,100 to $204,000.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 6, 2026.
This listingSenior Software Engineer, Training Efficiency$238K to $302K
Staff Machine Learning Engineer(TLM), Driver Understanding and Evaluation$238K to $302K
Head of Logistics$238K to $294K
Staff Technical Lead Manager, ML Sensor Validation$238K to $302K
Technical Lead Manager, Machine Learning, Maneuvering Tech$238K to $302K
Technical Lead Manager, Multiverse$238K to $302K
Staff Technical Lead Manager, Simulation, Machine Learning$238K to $302K
Chart as a table
| Role | Published base |
|---|---|
| Senior Software Engineer, Training Efficiency (this listing) | $238K - $302K |
| Staff Machine Learning Engineer(TLM), Driver Understanding and Evaluation | $238K - $302K |
| Head of Logistics | $238K - $294K |
| Staff Technical Lead Manager, ML Sensor Validation | $238K - $302K |
| Technical Lead Manager, Machine Learning, Maneuvering Tech | $238K - $302K |
| Technical Lead Manager, Multiverse | $238K - $302K |
| Staff Technical Lead Manager, Simulation, Machine Learning | $238K - $302K |
Observed changes
- Began meeting the published-salary criteria
- First observed on the employer's board
- Observed since
- Employer-stated posting date
- 2025-08-11
Where this pay comes from
Location as the employer wrote it: Mountain View, California, United States
$238,000—$302,000 USD
- Method
- Employer text, read with fixed rules and re-verified
- Checked
- Rule set
- us-base-v2
The pay passage appears in the employer's own job text. Fixed, deterministic rules located it, and the passage below was checked against the stored copy of the posting.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
