Research Internship - United States

About Flexion:

At Flexion, we’re building the intelligence layer powering the next generation of humanoid robots. Our mission is to accelerate the transition from fragile prototypes to real-world humanoid deployment. We are founded by leading scientists in robot reinforcement learning (ex-Nvidia, ex-ETH Zürich), and backed by leading international VC firms. In just months, we’ve gone from our first line of code to deploying real humanoid capabilities.Fifty of the world's best robotics researchers are already building the future at Flexion's headquarters in Zurich. Among them are Nikita Rudin, David Hoeller, Julian Nubert, and Korrawe Karunratanakul -  scientists who have redefined what humanoid robots can do. Now we are building their counterpart team in the United States, in San Francisco.

The Role:

If you are among the most capable and ambitious early-career robotics researchers, someone who has stared at the limits of what robots can do today and decided that simply wasn't good enough, we want to hear from you.

You will join Flexion's US research team as an intern, take on research problems that matter most, and deploy real solutions on real hardware. You will work directly with a world-class team across two continents on challenges that have no published answers yet. This internship is designed to give exceptional graduate researchers the opportunity to do their highest-impact work in a fast-moving industrial research environment, with a clear path to a full-time Research Scientist role following the internship.

Requirements

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

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