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Zoox is developing the first ground-up, fully autonomous vehicle fleet and supporting ecosystem. This role focuses on advancing sensor simulation fidelity through machine learning.
You will design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data in autonomous vehicle simulation pipelines. You'll collaborate with Synthetic Data and HIL Sim teams to integrate 3DGS and NeRF-based 3D reconstruction with simulation of all sensor modalities. Your work will include building and curating training datasets from real and simulated sensor data, leveraging GenAI-based data augmentation techniques.
You'll partner with Sim V&V and Perception engineers to define sensor realism metrics and close sim-to-real gaps with the greatest impact on autonomy. The role includes mentoring engineers and interns, and establishing best practices for training and deploying ML on Zoox sensor data.
This is a senior individual contributor role at the intersection of robotics, machine learning, and autonomous systems, requiring both technical depth and collaborative leadership.
Requirements:
- 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
