Machine Learning Engineer

About the Company

Human Archive is a research lab focused on modeling human embodied intelligence. Founded by Stanford and UC Berkeley researchers, we build custom hardware products and deploy them globally to recover human embodied intelligence as a learned model.

Responsibilities

• Build systems for multimodal perception, annotation, dataset QA, and robotics evaluation
• Publish research on multimodal data by fine-tuning and evaluating VLA models on downstream robotics tasks and policy performance
• Build post-training and reinforcement learning systems around robotics failure modes and corrective demonstrations
• Work across video understanding, tracking, pose estimation, temporal modeling, and multimodal alignment
• Develop tooling for benchmarking, observability, and temporal efficiency
• Prototype quickly, ship rapidly, and iterate from real-world robotics deployments and research feedback

Requirements

• Strong ML engineering fundamentals across robotics, computer vision, and perception systems
• Experience with video understanding, tracking, pose estimation, robotics, or real-world sensor systems
• Strong technical intuition and ability to move quickly in ambiguous research environments
• Demonstrated exceptional ownership in previous work

Preferred Qualifications

• Published research or production experience in robotics, embodied AI, reinforcement learning, motion capture, or vision systems