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Zoox

Senior / Staff Machine Learning Engineer - 3D Sensor Simulation

Hybrid · Foster City, CA

This hybrid role accepts applicants from: United States.

Locations: Foster City, CA · Seattle, WA

Apply on the employer's siteOriginal listingJSON

PeopleSeek Talent does not submit applications. The button opens the employer's own page; this listing was verified 1 hour ago.

PayUSD 242,000 – 348,000 per year
Work arrangementHybrid
Who can applyPeople in United States
LevelLead
TypeFull time
Occupationmaintenance and repair engineer
LanguageNot stated
Posted12 October 2026
Last verified1 hour ago
StatusActive

About the role

The Zoox 3D Sensor Simulation team’s work is at the core of creating in-the-loop simulation and high fidelity synthetic data for training and testing autonomous vehicle AI. We are looking for a machine learning professional with experience applying ML to sensor data, such as LiDAR, radar, and cameras.

This role lies at the intersection of machine learning and 3D simulation at Zoox, ranging from learned sensor models to neural 3D reconstruction. By improving the realism of simulated sensors (lidar, radar, and cameras), you’ll enable the Zoox robotics autonomy stack to learn, improve, and enhance the mobility of humans.

In this role, you will:

  • Design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data in autonomous vehicle (AV) simulation pipelines.
  • Collaborate with Synthetic Data and HIL Sim teams to integrate 3DGS and NeRF-based 3D reconstruction with simulation of all sensor modalities.
  • Build and curate training datasets from real and simulated sensor data, including GenAI-based data augmentation.
  • Partner with Sim V&V and Perception engineers to define sensor realism metrics and close the sim-to-real gaps with the greatest impact on autonomy.
  • Mentor engineers and interns, and establish best practices for training and deploying ML on Zoox sensor data.

Qualifications:

    • 2+ years of prior experience as a Technical Lead
    • 5+ years of experience developing ML models in Python with PyTorch or TensorFlow

    • Industry experience applying ML to sensor data (lidar, radar, and/or cameras)

    • Solid understanding of customizing, tuning, and validating ML training pipelines

Bonus Qualifications:

  • Experience with generative techniques such as diffusion, flow matching, or VAEs

  • Experience with 3D reconstruction (3DGS, NeRFs) or differentiable rendering

  • Prior experience developing autonomy in AVs or other robotics and computer vision

About ZooxZoox 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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AccommodationsIf 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.

Qualifications

  • 2+ years of prior experience as a Technical Lead
  • 5+ years of experience developing ML models in Python with PyTorch or TensorFlow
  • Industry experience applying ML to sensor data (lidar, radar, and/or cameras)
  • Solid understanding of customizing, tuning, and validating ML training pipelines
  • Experience with generative techniques such as diffusion, flow matching, or VAEs
  • Experience with 3D reconstruction (3DGS, NeRFs) or differentiable rendering
  • Prior experience developing autonomy in AVs or other robotics and computer vision

Listed via Zoox · first seen 22 October 2026 · last verified 28 October 2026

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