Senior Machine Learning Engineer - Autotuning at Zoox
Foster City, California, USA -
Full Time


Start Date

Immediate

Expiry Date

20 Nov, 25

Salary

277000.0

Posted On

20 Aug, 25

Experience

8 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Computer Science, Machine Learning, Python, Training

Industry

Information Technology/IT

Description

Zoox is looking for machine learning engineers to help build systems to evaluate and improve autonomous driving behaviors by learning from expert human drivers. Our team develops core technologies to benchmark our vehicles against expert human driving and tune driving software toward more human-like behaviors. These systems are critical for ensuring safe, comfortable, and natural driving experiences for our riders.

QUALIFICATIONS:



    • BS/MS/PhD in Machine Learning, Computer Science or related field and 8+ years of experience

    • Proficiency in Python and PyTorch, with experience building ML systems
    • Background in training, evaluating, and deploying ML models
    • Comfortable working with large datasets and distributed compute platforms
      $230,000 - $277,000 a year

    ABOUT ZOOX

    Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
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    How To Apply:

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    Responsibilities


      • Design and build ML systems that learn from expert human driving data to model human-like driving behaviors.

      • Develop evaluation methods that measure the human-likeness of autonomous driving.
      • Develop AutoML systems to optimize autonomous driving software and improve alignment with expert human driving
      • Work with large-scale datasets and distributed training pipelines to deliver production-ready ML solutions.
      • Collaborate cross-functionally with planner, simulation, and infrastructure teams to drive measurable improvements in vehicle behavior.
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