Machine Learning Engineer - 3D Sensor Simulation

The role

You will design, train and validate ML models that make simulated lidar, radar and camera data more realistic for autonomous vehicle simulation. You will work with synthetic data, perception and simulation teams, and mentor engineers and interns.

What you will do

• Design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data in 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 sim-to-real gaps
• Mentor engineers and interns, and establish best practices for training and deploying ML on Zoox sensor data

Required 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

Nice to have

• 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 the company

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem to bring it to market. It aims to provide next-generation mobility-as-a-service in urban environments.