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Zoox

Lead Sensor Systems Engineer

Foster City, CA
USD 139k - 280k
Matlab Python Machine Learning
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Description
The Lead Zoox Sensor Systems Engineer should be a highly self-driven and organized individual and is expected to work across the company in a cross-functional role. The goal is to ensure that our sensor suite satisfies high-level requirements and guarantees the safe and stable operation of the vehicle. The responsibility includes improving the requirement traceability of the existing sensor architecture as well as investigation into the next generation sensor architecture. This candidate should have a strong Systems Engineering mindset, as well as a good understanding of different sensing modalities (Camera, LiDAR and Radar) and how their data products are used by downstream software (perception, calibration, mapping etc).

Responsibilities

  • Communicate with cross-functional teams (Software, Electrical, Optical, Mechanical, System Design and Mission Assurance) to negotiate requirements 
  • Own the current and next generation sensor architecture
  • Establish and maintain the traceability of vehicle-level sensing requirements to subsystem-level sensor requirements
  • Perform System-Level trade studies 
  • Define V&V strategy for Sensor Systems and work with test engineers to define test plans. 
  • Interpret test results and evaluate system-level impact
  • Establish and enforce Systems Engineering processes

Qualifications

  • B.S./M.S./PhD in Engineering, Physics or relevant field and 5+ years of experience
  • Experience in a Systems Engineering role
  • Technical understanding of the physics and fundamental technologies used in automotive Camera, LiDAR, and Radar systems
  • Experience in negotiating, writing and managing requirements
  • Cross-functional collaboration to determine system requirements, through a combination of field testing or analysis and experience in presenting results

Bonus Qualifications

  • Experience on Sensors for Autonomous Vehicles (Camera, LiDAR, Radar)
  • MATLAB, Python, Optis (or comparable integrated raytracing tool)
  • Experience in Sensors V&V
  • Experience on Safety-Critical Systems
Compensation
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. The salary range for this position is $139,000 to $280,000. A sign-on bonus may be offered as part of the compensation package. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
 
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 accommodations@zoox.com 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.
Zoox
Zoox
Autonomous Vehicles Machine Learning Robotics Transportation

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