Research Internship - United States

About the role

The role is an internship for exceptional early-career robotics researchers. You will join Flexion's US research team, tackle impactful research problems, and deploy real solutions on real hardware. You will work with a world-class team across two continents on challenges with no published answers.

Responsibilities

• Take on research problems that matter most
• Deploy real solutions on real hardware
• Work directly with a world-class team across two continents
• Contribute to challenges with no published answers

Requirements

• Ongoing PhD in Robotics, Machine Learning, or a closely related field, or a recently completed Master's degree with exceptional research output
• Demonstrated experience deploying learning-based controllers on real robotic hardware, or strong research signal that you can do so quickly
• Strong working knowledge in: Reinforcement learning, Physics-based simulation (Isaac Gym/Lab, MuJoCo, or equivalent), Python and PyTorch, including training neural networks at scale
• Knowledge in at least two of the following: Diffusion models, Flow matching, Dexterous manipulation, Sim-to-real transfer and real-to-sim calibration, Whole-body control and loco-manipulation, Synthetic data generation for robot learning, Vision Language Model Fine-Tuning (SFT and RL-based), Transformer-based 3D Scene Understanding

Qualifications

Must-haves:

• Ongoing PhD in Robotics, Machine Learning, or a closely related field, or a recently completed Master's degree with exceptional research output
• Demonstrated experience deploying learning-based controllers on real robotic hardware, or strong research signal that you can do so quickly
• Strong working knowledge in: Reinforcement learning, Physics-based simulation (Isaac Gym/Lab, MuJoCo, or equivalent), Python and PyTorch, including training neural networks at scale
• Knowledge in at least two of the following: Diffusion models, Flow matching, Dexterous manipulation, Sim-to-real transfer and real-to-sim calibration, Whole-body control and loco-manipulation, Synthetic data generation for robot learning, Vision Language Model Fine-Tuning (SFT and RL-based), Transformer-based 3D Scene Understanding

Skills

• Reinforcement learning
• Physics-based simulation (Isaac Gym/Lab, MuJoCo, or equivalent)
• Python and PyTorch
• Diffusion models
• Flow matching
• Dexterous manipulation
• Sim-to-real transfer and real-to-sim calibration
• Whole-body control and loco-manipulation
• Synthetic data generation for robot learning
• Vision Language Model Fine-Tuning (SFT and RL-based)
• Transformer-based 3D Scene Understanding

Benefits

• Competitive compensation package
• A front-row seat at one of the world's most ambitious robotics companies
• An energetic, collaborative team with a relentless bias for action
• The opportunity to build something no one has ever done in this field - alongside the world's leading researchers

Pay

TBD

Schedule

TBD