Internship - Humanoid Motion Generation (Diffusion or Flow Matching)

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 deployment of humanoids. We are founded by leading scientists in robot reinforcement learning (ex-Nvidia, ex-ETH Zürich) and backed by leading international VC firms. Within a few months, we’ve gone from our first line of code to deploying real humanoid capabilities with our partners.

• The Role:
• We’re looking for a talented MS or PhD intern to join our team in Zürich and push the boundaries of how humanoid robots perceive and interact with the world.
• You will work at the intersection of generative whole-body trajectory generation (using diffusion models, flow matching, and/or auto-regressive models) and 3D environment perception.
• If you enjoy solving open-ended problems in a fast-moving environment, this is a unique opportunity to learn from our world-class team of engineers and scientists, and contribute to our foundational software development.

Requirements

• Required skills:
• Currently pursuing a Master’s or PhD degree in Computer Science, Robotics, Machine Learning, or a related technical field.
• Experience in diffusion models/flow matching or learning-based trajectory generation with relevant project experience.
• Familiarity with generative modeling and its application to time series data.
• Excellent Python and PyTorch skills; comfortable writing clean, modular code for rapid experimentation.
• Strong, proven analytical thinking and problem-solving skills.
• Ability to navigate and implement state-of-the-art methods from recent literature.
• Preferably: autonomous research/project work in human motion generation (neural avatars), robotic imitation learning, or ego-centric action prediction.
• Additionally, the following skills are a plus but not required:
• Experience in robotics.
• Experience with simulation environments.
• Experience with multi-modal generative models.
• Experience with finetuning foundation models, e.g., Gr00t or SmolVLA, to produce whole-body actions or kinematics motions.

Benefits

• Competitive compensation
• A front-row seat at one of Europe’s most ambitious robotics companies
• An energetic, collaborative team with a bias for action