Machine Learning Engineer, Zoox

• We are looking for experts who have hands-on experience building multi-modality foundation models—whether that involves AV-centric modalities (Vision, LiDAR, Radar) or broader domains (Vision, Language, Text, Audio)
• MS or PhD in Computer Science, Machine Learning, or a related technical field with demonstrated professional experience
• Deep, proven expertise in building and training large-scale multi-modality foundation models (e.g., Vision-Language Models (VLMs), Vision-Audio-Text, or Vision-LiDAR-Radar architectures)
• Strong understanding of cross-modal alignment, multi-modal attention mechanisms, and large-scale pre-training techniques
• Proven experience in Knowledge Distillation (KD), model compression, and training highly efficient student models for production environments
• Proficiency in ML frameworks (e.g., PyTorch) and experience building large-scale ML training and evaluation pipelines
• You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills

Desirable

• The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence. As a Multi-modality Foundation Model Engineer, you will focus on building highly efficient, production-ready multi-modality models
• You will design, train, and deploy these models using Knowledge Distillation (KD) to transfer capabilities from large-scale proprietary teacher models to efficient student models capable of real-time, on-vehicle inference
• Build, pre-train, and evaluate large-scale multi-modality foundation models from the ground up, successfully aligning diverse data streams (e.g., Vision, LiDAR, Radar, Language, Audio)
• Define and execute the ML roadmap for deploying these multi-modality representations to the vehicle
• Architect and implement Knowledge Distillation pipelines to compress large-capacity multi-modal teacher models into highly efficient, production-ready student models
• Build high-quality training and evaluation datasets, applying advanced data-centric techniques to maximize cross-modal representation learning and student model convergence
• Collaborate with downstream perception teams to integrate and validate the performance, robustness, and latency of your models in on-board production systems