AI Research Scientist

The PICO Interactive team is redefining how people connect with virtual worlds, using computer vision, deep learning, SLAM, 3D reconstruction, and multi-sensor fusion to enable bare-hand tracking, eye tracking, and XR accessory interaction in real time. The challenge is making these systems work reliably across every lighting condition, movement speed, and use case, on constrained wearable hardware. When tracking breaks, the whole interaction falls apart. You will build the perception stack that shapes what natural, intuitive XR interaction feels like for millions of users. We are looking for a Research Scientist to advance these capabilities through algorithms, data, and practical systems. - Drive research in one or more of the following areas, based on your expertise and project priorities: - Develop human motion and multimodal interaction understanding algorithms, including pose estimation, tracking, and intent recognition. - Advance hand-object interaction understanding, including contact, grasping, manipulation, and interaction sequences. - Build human demonstration data and annotation pipelines for embodied AI, using reconstruction, automated labeling, weak supervision, and synthetic data generation. - Explore joint optimization of hand tracking, SLAM, and scene geometry to improve spatial consistency and robustness. Translate research into practical capabilities: - Develop rigorous evaluation methods and scalable systems, including real-time XR inference and data pipelines for embodied AI. - Collaborate across hardware, engineering, data, product, and UX teams; contribute publications, patents, and reusable tools. Minimum Qualifications - PhD in Computer Science, Electrical Engineering, Robotics, or a related discipline. - Research depth in at least one relevant area: 3D vision, human motion, hand-object interaction, SLAM, embodied perception, or multimodal learning. - Strong foundations in machine learning and relevant geometry, optimization, or temporal modeling methods. - Proficiency in Python and modern deep learning frameworks such as PyTorch. - Demonstrated research or technical impact through publications, systems, datasets, or products, with strong experimental and collaboration skills. Preferred Qualifications - Experience with egocentric perception, human interaction understanding, multi-view geometry, or sensor fusion. - Experience with human demonstration datasets, automated annotation, synthetic data, or learning from human interactions. - Experience with NLP, LLM fine-tuning, or vision-language fusion; experience in intelligent interaction technologies, including gesture recognition, voice interaction, multimodal input, or AI-powered interfaces, is a plus. - Experience with lightweight model deployment on XR or embedded devices. - Publications at leading vision, ML, HCI, or robotics venues, or experience translating research into practical applications.