Waymo—formerly the Google self-driving car project—makes it safe and easy for people & things to get around with autonomous vehicles. Take a ride now. Backed by Bessemer, Kleiner Perkins and Sequoia.
About the role
As a Software Engineer, you will solve ill-defined problems at the intersection of statistics, machine learning, and scalable systems engineering. You will develop creative quantitative models to address eval challenges, while providing rigorous mathematical and experimental proof of the correctness and statistical efficiency of your solutions. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- BS in Math, Physics, or another quantitative field
- Fluency in implementing statistical, ML, and general mathematical algorithms for large datasets
- Experience adapting techniques in research papers to applied problems
- Strong self-motivation and comfort with ambiguity to navigate complex systems and pursue open-ended problems to completion
- 3-5 years of experience with
- Navigating and modifying a large code base containing a variety of languages, such as C++, Python, and SQL
More about 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.
As a Software Engineer, you will solve ill-defined problems at the intersection of statistics, machine learning, and scalable systems engineering. You will develop creative quantitative models to address eval challenges, while providing rigorous mathematical and experimental proof of the correctness and statistical efficiency of your solutions.
- Tackle ill-defined, ambiguous problems and reduce complex systems into tractable quantitative models.
- Develop statistical techniques that enable...
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