Senior Machine Learning Engineer - Collision Avoidance System

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about 2 years old

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The Collision Avoidance System (CAS) is responsible for detecting and reacting to imminent collision situations in support of our vehicle’s overall safety goals. CAS Perception is responsible for processing raw sensor data from our vehicle’s world-class sensor suite using a combination of geometric, interpretable algorithms and deep learning to detect near-collisions with obstacles along our intended driving path, in the most challenging dense urban environments and under tight compute resource constraints. Overall, CAS is parallel and complementary to our Main Artificial Intelligence (AI) autonomy stack, and has a close relationship with our vehicle hardware and safety teams in order to architect redundancy into our overall driving system.

Responsibilities

  • You will lead a team of deep learning engineers leveraging technical and managerial skills to deliver high-impact results
  • You will set the short and long term technical direction for the team and collaborate on the broader company-wide directions
  • You will coordinate cross-functional initiatives with other teams across CAS, Systems Engineering, QA, and more
  • You will develop new algorithms that consume raw sensor data input to detect dynamic entities in the world
  • You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
  • You will engineer software that runs on-vehicle to efficiently execute our algorithms in real time
  • You will develop metrics and tools to analyze errors and understand improvements of our systems
  • You will collaborate with engineers on the other parts of CAS Perception, CAS Verification & Validation, CAS Planner, and the Main AI teams to solve the overall Autonomous Driving problem in complex urban environments

    • Qualifications

  • BS, MS, or PhD degree in computer science or related field
  • Experience with training and deploying Deep Learning models
  • Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
  • Fluency in C++
  • Extensive experience with programming and algorithm design
  • Strong mathematics skills
  • 8+ years of experience in a related field

    • Bonus Qualifications

  • Conference or Journal publications in Machine Learning or Robotics related venues
  • Prior experience with Prediction and/or autonomous vehicles in general
  • Fluency in Python
    • Compensation
    There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. The salary will range from $221,000 - $319,000. A sign-on bonus may be part of a compensation package. Compensation will vary based on geographic location, job-related knowledge, skills, and experience.  

    Zoox also offers a comprehensive package of benefits including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

    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.
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