Manager, ML Planner

AI overview

Lead the Offline Driving Intelligence team in developing machine learning algorithms to enhance driving models and collaborate across various partner teams to drive innovation.
The Offline Driving Intelligence team is responsible for developing machine learning algorithms that leverage data at large scale to train driving models. On the behaviors and planning team we learn and predict behaviors from large scale expert data and large scale reinforcement learning. This Offline Driving Intelligence team can leverage provided setups such as extended memory, flexible time and access to privileged information. The solutions developed are used in offline applications and to influence onboard decisions. This team is also building intelligent driving agents and dense driving metrics. In this role, you will collaborate closely with the Onboard Planning, Simulation, Perception, Validation, Data Science, Systems Engineering, QA, and ML Infra teams. In this role, you will:
  • Team Leadership: Manage, mentor, and grow a team of individual contributors, fostering a culture of innovation and continuous improvement.
  • Strategy Development: Develop and organize our overall strategy for Offline ML Models for planning, behaviors and agents. You will interface with multiple partner teams to identify opportunities for model adoption within their problem area. You’ll be setting the short and long term technical direction for the team and collaborate on the broader company-wide directions.
  • Technical Oversight: Provide technical guidance and leadership in the design and development of training models at large scale and work with partner teams on ensuring their efficient inference.
  • Performance Monitoring: Establish and monitor key performance indicators (KPIs) to measure the effectiveness of optimization strategies and drive continuous improvement.
  • Resource Management: Manage the allocation of resources within the team, ensuring that projects are staffed appropriately and that team members have the necessary tools and support to succeed.
  • Qualifications
  • Expertise with Reinforcement Learning and ML for Planning.
  • Extensive experience with programming and algorithm design, strong mathematics skills.
  • BS, MS, or PhD degree in computer science or related field.
  • 5+ years of experience with production Machine Learning pipelines, with at least 2 years in a leadership or management role.
  • Bonus Qualifications
  • Conference or Journal publications in Machine Learning or Robotics related venues.
  • Prior experience working with autonomous vehicles or robotics, diffusion models, large scale training.
  • 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.


    Accommodations
    If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.

    A Final Note:
    You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

    Perks & Benefits Extracted with AI

    • Health Insurance: health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance
    • Paid Time Off: paid time off (e.g. sick leave, vacation, bereavement), unpaid time off

    Zoox an American autonomous vehicle company currently headquartered in Foster City, California with multiple offices of operations throughout the San Francisco Bay Area.

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    Salary
    $242,000 – $333,000 per year
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