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
- 5+ 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:
- 7+ years of industry experience (or 3+ years post-doc experience) in a quantitative engineering role. This includes a proven track record as a technical lead navigating complex, multi-language codebases (C++, Python, SQL) to drive the end-to-end experimental lifecycle: developing hypotheses, designing and executing large-scale experiments, and building robust data pipelines
- 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.
Top Skills
Similar Jobs
.png)
What you need to know about the NYC Tech Scene
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
