Lead - Dexterous Manipulation job opportunity at Flexion Robotics.

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Date2025-12-22T13:44:44.282Z bot
Flexion Robotics Lead - Dexterous Manipulation
Experience: General
Pattern: Full-time
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degreePhD
loacation Zürich, Switzerland
loacation Zürich....Switzerland

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. The Role: We are seeking a Manipulation Lead to define and drive Flexion's manipulation stack, with a strong focus on learning-based dexterous control for humanoid robots. In this role, you will own the technical direction, architecture, and delivery of manipulation capabilities, from research ideas to real-time execution on physical robots. You will work closely with the perception, controls, and infrastructure teams, and lead the engineers working on dexterous manipulation. This is a hands-on technical leadership role, not a purely managerial position. Responsibilities Own the full manipulation stack, from problem formulation and data collection to training, deployment, and evaluation on real robots. Define the technical roadmap for dexterous manipulation. Lead the transition from research prototypes to robust, real-time controllers running on humanoid hardware. Design, implement, and deploy learning-based manipulation controllers (e.g. diffusion-based policies, flow matching, RL, or hybrids). Establish best practices for training, sim-to-real transfer, evaluation metrics, and debugging on hardware. Mentor and guide the engineers working on manipulation. Collaborate closely with perception, whole-body control, and infrastructure teams to enable closed-loop manipulation. PhD degree in Robotics, Machine Learning, or a related field, with significant hands-on experience in learning-based manipulation. Deep expertise in dexterous manipulation models, such as: Diffusion models Flow matching Reinforcement learning Strong understanding of robot control and real-time constraints. Proven experience deploying learning-based controllers on real robotic hardware. Excellent proficiency in Python and PyTorch, including training large neural networks. Solid knowledge of transformers and modern generative models. Ability to take technical ownership and make high-impact decisions in a fast-moving environment.

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