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Software Engineer, Statistical Evaluation and Sampling
Waymo · Mountain View, CA, USA; San Francisco, CA, USA · Remote · Active · $170,000–$216,000 / year · Greenhouse
Job facts
| Field | Value |
|---|---|
| Company | Waymo |
| Title | Software Engineer, Statistical Evaluation and Sampling |
| Normalized title | - |
| Department / team | Simulation (7XW) |
| Location | Mountain View, CA, United States |
| Work model | Remote / Remote |
| Employment type | - |
| Salary | $170,000–$216,000 / year |
| Status | active |
| ATS provider | Greenhouse |
| Posted / first seen | 2026-01-20 / 2026-05-30 |
| Changed / last seen | 2026-06-06 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Waymo. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Greenhouse. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in Mountain View. | Open |
| Department jobs | Active postings in Simulation (7XW). | Open |
| Work model jobs | Active Remote postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Waymo |
| Source | 44b26b6c-8dfd-437f-a75f-2aba3142363b |
| ATS provider | Greenhouse |
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.
Waymo's Release Evaluation org ensures that each version of the Waymo Driver is safe before it hits the road. We build automated pipelines to solve the long tail of rare and exceptional scenarios in autonomous driving, looking for needles in a haystack under both time and resource constraints. Within Release Evaluation, the Sampling and Efficiency team applies importance sampling techniques and machine learning to maximize the statistical efficiency of these discovery pipelines.
You will:
Develop importance sampling techniques that enable our evaluation pipelines to deliver better signals with fewer resources.
Find signals in our logs and simulations that might help us to more efficiently discover rare and important events.
Build systems that systematically optimize multiple objectives under resource constraints.
Collaborate with other engineers, data scientists, statisticians and the leadership team to deliver evaluation products and help make data driven decisions.
Champion code health and best practices in a large and complex code base
You have:
BS in Computer Science, Robotics, Statistics, Physics, Math or another quantitative area
Fluency with probability and statistics
Strong self-motivation to navigate complex systems and pursue open-ended problems to completion
2-3 years of experience with
Navigating and modifying a large code base containing a variety of languages, such as C++, Python and SQL
Performing statistical analyses
Building data processing pipelines
Writing, reviewing, and merging code following industry standards for code health and maintainability
We prefer:
Experience programming in C++
Experience developing and evaluating sampling methods
Experience designing, training, evaluating, and applying ML models
Experience working in the AV industry
PhD in a quantitative field
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 $170,000 — $216,000 USD
Full job record
| Job ID | b9879ac094bb12a8ba0c036c662454e95db2b6ef |
| Org ID | 3fcf3bd1-54bc-4952-918e-d9d9f3354837 |
| Source ID | 44b26b6c-8dfd-437f-a75f-2aba3142363b |
| Board ID | 44b26b6c-8dfd-437f-a75f-2aba3142363b |
| Provider | greenhouse |
| Provider Job Key | 7543520 |
| Title | Software Engineer, Statistical Evaluation and Sampling |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | Mountain View, CA, USA; San Francisco, CA, USA |
| Department | Simulation (7XW) |
| Team | — |
| Employment Type | — |
| Workplace Type | remote |
| Remote Policy | remote |
| Country | United States |
| Region | CA |
| City | Mountain View |
| Salary Raw | Salary Range $170,000 — $216,000 USD |
| Salary Min | 170,000 |
| Salary Max | 216,000 |
| Salary Currency | USD |
| Salary Period | year |
| Source URL | https://careers.withwaymo.com/jobs?gh_jid=7543520 |
| Apply URL | https://careers.withwaymo.com/jobs?gh_jid=7543520 |
| First Seen At | 2026-05-30 08:06:00Z |
| Last Seen At | 2026-06-06 20:03:15Z |
| Last Checked At | 2026-06-06 20:03:15Z |
| Last Changed At | 2026-06-06 07:32:55Z |
| Inactive At | — |
| Source Posted At | 2026-01-20 19:28:49Z |
| Source Updated At | 2026-06-04 18:47:38Z |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=greenhouse/board=waymo/date=2026-06-06/2026-06-06T20-03-14-482Z-9ae82cf0fe9f70799413abab7bb6e36d8ef075138c44691cb24f99899f7f0674.json |
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