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
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