Software Engineer, Evaluation Infrastructure and Internal Tooling

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Waymo is an autonomous driving technology company with a mission to make it safe and easy for people and things to get where they’re going. Since our start as the Google Self-Driving Car Project in 2009, Waymo has been focused on building the World’s Most Experienced Driver in hopes of improving the world's access to mobility while saving thousands of lives now lost to traffic crashes. Our Waymo Driver powers Waymo One, our fully autonomous ride-hailing service, as well as Waymo Via, our trucking and local delivery service. To date, Waymo has driven over 20 million miles autonomously on public roads across 25 U.S. cities and conducted over 20 billion miles of simulation testing.

The Planner Evaluation team is working on one of the most challenging problems in autonomous driving: evaluating whether our autonomous cars are good drivers.  Beyond keeping our cars from colliding with things, we need to ensure that they don’t scare other drivers, pedestrians, or other people on the road (or their own passengers), that the ride they provide is comfortable, and that they’re following the rules of the road. By creating the metrics that define what good driving is, we can influence and improve onboard code both indirectly (by providing the quality signals for onboard engineers to aim for) and directly (by working with onboard engineers to incorporate signals into our motion planning code). At Waymo we have a ton of extremely rich data from which to extract relevant signals--if you like working with data, geometric reasoning, simulation, and/or machine learning, this is the place for you!

 

The Evaluation Infrastructure and Internal Tooling role is responsible for building infrastructure and tooling required for data driven, at scale behavioral evaluation of the planning software. 

 

In this role you will:

  • Work cross-functionally with data scientists and onboard engineers across the Planner team;
  • Build data pipelines for signal discovery, data labeling, feature extraction and metric computation based on large-scale simulations;
  • Create tooling for metric aggregation, reporting, and visualization that are tailored for behavioral evaluation;
  • Spin up continuous monitoring and validation of metric signals, detect anomalies and identify root causes.

 

We'd like you to have:

  • MS in Computer Science
  • 5 years of industry experience 
  • Passion for infrastructure work: building internal tools, frameworks, and libraries
  • Excellent Python coding abilities, especially for modeling and/or statistical computing 
  • System Design, experience building and architecting large-scale, complex systems 
  • Experience with tools for manipulating big data
  • Problem-oriented mindset instead of only a solutions-oriented approach


It's preferred if you have:

  • PhD in Computer Science
  • 7 years of industry experience
  • Comfort with writing production code in C++
  • Experience with monitoring, observability, alerting
  • Experience with machine learning or large scale statistical analysis
  • Experience in robotics or with spatial data in another field 

Note: The following is only applicable for roles hired in the US. As we keep the safety of our employees and our communities top of mind, COVID-19 vaccinations continue to be a critical prevention measure to help end the pandemic.  Because of this, Waymo requires all US-based employees who perform work onsite for Waymo to be fully vaccinated against COVID-19.  If you are unable to be vaccinated due to a disability, medical condition or religious belief, Waymo will provide a reasonable accommodation consistent with federal, state, and local law.

More Information on Waymo
Waymo operates in the Automotive industry. The company is located in Mountain View, CA. Waymo was founded in 2009. It has 2359 total employees. It offers perks and benefits such as Dental insurance, Vision insurance, Health insurance, Life insurance, 401(K) and Employee stock purchase plan. To see all 31 open jobs at Waymo, click here.
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