Robot Transfer Learning Expert(PHD)

Responsibilities - Develop and evaluate transfer learning approaches for robotic systems across domains, tasks, and embodiments - Apply sim-to-real and real-to-real transfer techniques to manipulation, locomotion, or perception tasks - Build and fine-tune foundation models and pre-trained representations for downstream robot learning tasks - Design experiments to benchmark transfer performance across diverse robotic environments and hardware configurations - Document methodologies, experimental results, and technical approaches clearly and reproducibly

Required qualifications - Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., RSS, CoRL, ICRA, NeurIPS, ICML, or equivalent) - Master's or PhD in Robotics, Computer Science, Electrical Engineering, or a related quantitative field - Demonstrated expertise in transfer learning, domain adaptation, or sim-to-real methods applied to robotic systems - Strong problem-solving skills and ability to work independently on technical and research tasks

Preferred qualifications - Experience with robotic simulation environments (e.g., IsaacGym, MuJoCo, PyBullet, Gazebo, or similar) - Familiarity with robot learning frameworks and pre-trained models (e.g., RT-2, OpenVLA, Octo, or similar) - Background in TA'ing or teaching robotics, reinforcement learning, or machine learning courses

Why apply - Flexible Time Commitment – Work on your schedule while tackling meaningful robotics challenges - Startup Exposure – Work directly with an early-stage Y Combinator-backed company, gaining hands-on experience that sets you apart - Exceptional Pay – Project-based pay ranges from $150–$200/hour - Portfolio Building – Gain experience on frontier robot learning and transfer problems - Professional Growth – Sharpen your skills on varied, real-world robotic datasets and systems

Employment type: Contract Time commitment: 10 hours/week Location: Remote