Machine Learning Engineer - Vision Foundation Models

Overview

The Perception team at Zoox is responsible for how our purpose-built robotaxi understands the world, including what happens inside the cabin. In this role, you will design, train, and evaluate vision-based in-cabin perception models that understand passengers, objects, and behaviors inside the vehicle. These models support rider safety and the operations that keep each ride running smoothly.

You will independently own a model area end to end, from data curation and training through evaluation and deployment under real-time and compute constraints, working closely with partners across the company.

Responsibilities

• Design, train, and evaluate vision models that understand passengers, objects, and behaviors inside the vehicle, across detection, pose, and action recognition.
• Independently own a model or feature area end to end: curate data, define labeling guidelines, train and evaluate models, and take them through deployment.
• Set the quality bar for the models you own, using rigorous evaluation and error analysis so they are reliable enough to support live operations.
• Deploy models under real-time and compute constraints shared with the rest of the onboard perception system.
• Partner across product, operations, safety, and vehicle software to turn requirements into model specifications, metrics, and validation plans.

Qualifications

• MS or PhD in Computer Science, Machine Learning, Computer Vision, or a related field with 6+ years of industry experience (PhD with 3+ years).
• Deep expertise in computer vision and deep learning for image and video understanding: object detection, pose and action recognition, and fine-grained classification, with hands-on experience with modern architectures (convolutional and vision-transformer detectors, and self-supervised or foundation-model backbones such as DINOv3).
• Strong proficiency in Python and PyTorch for model design, training, and evaluation.
• Experience with vision-language models or foundation-model backbones, and with auto-labeling or synthetic data for long-tail and rare classes.
• Track record of owning the full ML lifecycle on real-world data: dataset curation and labeling guidelines, training, metric design (precision, recall, and false-positive rate at an operating point), and error analysis.

Bonus Qualifications

• Experience with in-cabin or occupant monitoring (occupant or driver monitoring systems), ideally including multi-camera or fisheye imagery and low-light conditions.
• Experience deploying deep learning models to real-time, resource-constrained systems (robotics, automotive, or edge devices) with TensorRT / ONNX or similar.
• Proficiency in C++ for model integration.