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Zoox is developing a fully autonomous vehicle fleet and is seeking a Senior Machine Learning Engineer to advance the realism of simulated sensor data in their autonomous vehicle simulation pipelines.
In this role, you will design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data. You'll collaborate with Synthetic Data and Hardware-in-the-Loop (HIL) Simulation teams to integrate 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF)-based 3D reconstruction with simulation of all sensor modalities. You will build and curate training datasets from real and simulated sensor data, including leveraging generative AI-based data augmentation techniques.
You'll partner with Simulation Verification & Validation (V&V) and Perception engineering teams to define sensor realism metrics and systematically close sim-to-real gaps that have the greatest impact on autonomy performance. As a senior engineer, you will mentor other engineers and interns, establish best practices for training and deploying ML models on Zoox sensor data, and help shape the technical direction of the sensor simulation platform.
This is a hands-on technical role at the intersection of machine learning, computer vision, 3D reconstruction, and autonomous vehicle simulation. You'll work on cutting-edge generative techniques and sensor physics modeling to accelerate AV development.
Requirements:
- 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 autonomous vehicles or other robotics and computer vision applications
