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 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.

Requirements

Must-haves

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.
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
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

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aimachine learningmlpython

Platform: lever
Effort: quick

Benefits

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