Machine Learning Engineer - 3D Sensor Simulation
Foster City, CA
- Pay
USD 189,000–283,000/year — pay source
Salary 189,000 – 283,000 USD per year About Zoox
Read the full posting- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingDesign, train, and validate ML models for realistic simulated lidar, radar, and camera data
Integrate 3DGS and NeRF-based 3D reconstruction with simulation of all sensor types
Build and curate training datasets from real and simulated sensor data, including GenAI augmentation
Define sensor realism metrics and close sim-to-real gaps with Sim V&V and Perception engineers
Mentor engineers and interns on best practices for training and deploying ML on sensor data
From the employer’s posting
Design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data
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
Mentor engineers and interns, and establish best practices for training and deploying ML on Zoox sensor data.
What you’ll bring
All qualificationsCore experience
- 3+ years of experience developing ML models in Python with PyTorch or TensorFlow
- Experience with generative techniques such as diffusion, flow matching, or VAEs
- Solid understanding of customizing, tuning, and validating ML training pipelines
- Experience with 3D reconstruction (3DGS, NeRFs) or differentiable rendering
- Prior experience developing autonomy in AVs or other robotics and computer vision
Qualification wording
3+ years of experience developing ML models in Python with PyTorch or TensorFlow
Experience with generative techniques such as diffusion, flow matching, or VAEs
Solid understanding of customizing, tuning, and validating ML training pipelines
Experience with 3D reconstruction (3DGS, NeRFs) or differentiable rendering
Prior experience developing autonomy in AVs or other robotics and computer vision
Tools in this posting
- Python
- PyTorch
- TensorFlow
Source — Tool mentions in context
Qualifications: - 3+ years of experience developing ML models in Python with PyTorch or TensorFlow - Industry experience applying ML to sensor data (lidar, radar, and/or cameras)
About Amazon
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market.
In the employer’s words · Read in context
Posting record
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- Live
- First seen by us
- Oct 13, 2026 · 16 days ago
- Recorded sightings
- 37
- Last seen by us
- Oct 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
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Job description
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:
- 3+ 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
Salary
189,000 – 283,000 USD per year
Employment type
Full-time
Source details
Source notes
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- Pay
Salary 189,000 – 283,000 USD per year About Zoox
- Location & working pattern
Foster City, CA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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