Develop advanced Vision-Language-Action models for vehicle perception to enhance safety and driving suggestions, utilizing real-time data on the innovative robotaxi platform.
Design and train Vision-Language-Action (VLA) solutions for robotaxis
Lead end-to-end data strategy, including mining, auto-labeling, and dataset construction to power our ML flywheel
Lead the full post-training stack for VLMs and VLAs, including Continual Pre-training (CPT) on domain-specific driving data, Supervised Fine-Tuning (SFT) for instruction following.
Utilize our large-scale data pipelines and ML infrastructure to research, prototype, and deploy solutions that improve driving behavior
Partner with cross-functional teams to integrate perception signals
MS or PhD in Computer Science or related field
Background in deep learning solutions for VLM and VLA models
Track record in post-training large-scale models, CPT, SFT, RL
Hands-on experience with production ML pipelines, including dataset creation, training frameworks, and metrics
Expertise in Python libraries (PyTorch, NumPy, Pandas, VLLM)
Deep knowledge of cutting-edge computer vision techniques
Publications in top-tier conferences (CVPR, ICCV, RSS, ICRA)
Experience with integrating large language models to various tasks.
Zoox is building a fully autonomous vehicle fleet from the ground up, coupled with the ecosystem necessary to launch this technology into urban environments. By integrating robotics, machine learning, and innovative design, Zoox is paving the way for a new era of mobility-as-a-service.
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