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Job description
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 robotics researchers in the country, 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 the first wave of US research scientists at Flexion, set the agenda together, own the 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.
Key Responsibilities
Drive original research in learning-based robotics - from concept to hardware deployment
Own your research direction: define problems, design experiments, and iterate fast
Collaborate directly with Flexion’s Zurich research team across Manipulation, Motion Generation, and Perception
Contribute to a shared culture of scientific rigour, velocity, and impact
Qualifications
PhD in Robotics, Machine Learning, or a closely related field - completed or near completion
Proven track record of deploying learning-based controllers on real robotic hardware
Expert-level knowledge in: Reinforcement learning
Physics-based simulation (Isaac Gym/Lab, MuJoCo, or equivalent)
Python and PyTorch, including training large-scale neural networks
Deep expertise in at least two areas 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
Comprehensive health and retirement plan benefits
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
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Worksite address
san francisco, CA, 94199, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.