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Zoox

Senior Machine Learning Engineer - Perception

Foster City, California, United States +1

Location
Foster City, California, United States · Boston, Massachusetts, United States
Work
Hybrid
Employment
Full-time
Level
Senior
Function
Data & AI
Pay
$225K – $305K a year
Posted
17 months ago

About the role

As a Perception Engineer, you will be instrumental in designing and implementing cutting-edge detection models, utilizing fused sensor data. In particular, the role will be on advanced projects that are aimed at the next generation of driving software, including looking at joint detection and tracking models as well as ways to send rich embeddings to downstream models to relay semantic meaning and capture uncertainty.

This role offers a unique opportunity to directly impact Zoox's expansion into new areas. By developing more generalizable and adaptable models, you will help us navigate diverse and challenging circumstances, accelerating our mission to deliver safe and reliable autonomous mobility. We are looking for a highly motivated individual passionate about pushing the boundaries of perception in the autonomous vehicle space.

In this role, you will

  • Train ML models, perform various studies into incorporating SOTA approaches as well as ablations/modifications of existing models
  • Document experimental outcomes and drive towards deploying these models into Zoox robots
  • Visualize your data at the input and output to understand the issues
  • Collaborate with teams such as ML optimization, data labeling, metrics and planner to ensure project is successful for Zoox releases

Qualifications

  • MS/PhD in Computer Science or related field
  • Experience with PyTorch or related ML frameworks
  • Experience deploying learned models into production
  • Excellent collaboration and teamwork skills with proven track record of working on large-scale problems

Bonus Qualifications

  • Prior experience in the AV or related space with joint detection and tracking models
  • Deep understanding of how to setup end-to-end training and evaluation processes

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.

Follow us on LinkedIn

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.

Base Salary Range

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. 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.

Source

Posted by Zoox on their own hiring page, jobs.lever.co. OptiTalent shows it as published, read 07 Nov 2026 02:50 UTC; the original is the reference.

View the original ad ↗

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