Robot Transfer Learning Expert(PHD)

Job Description

• This is a remote, project-based role for robotics researchers and engineers with deep expertise in transfer learning applied to robotic systems. You will complete tasks at the intersection of robot learning and domain adaptation — including model development, sim-to-real transfer, and research tasks applied to manipulation, locomotion, or perception pipelines. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge robotics research, and a strong addition to your research portfolio.

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

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

Company Description

• AfterQuery is a research lab investigating the boundaries of artificial intelligence through novel datasets and experimentation. We're backed by top investors, including Y Combinator and Box Group, and support all leading AI labs.