The roleWe are seeking an expert in dexterous manipulation and large-scale modeling to lead the development of our physical foundation models. The goal of this position is to leverage internet-scale egocentric video to build Vision-Language-Action (VLA) models that enable our humanoid robots to interact with the world with human-like fluidity. You bring a deep understanding of how to bridge the gap between observing human actions in video and executing high-DOF (20+) motor control.
As a Research Engineer for Dexterous Manipulation at Flexion, you'll work in our Zürich office to develop and deploy state-of-the-art learning-based controllers. You will take ownership of the model architecture, integrating egocentric priors with real-time robot policies, to ensure our hardware can manipulate objects reliably and flexibly in unstructured environments.
Key Responsibilities- Scalable Egocentric Pre-training: Architect and implement large-scale pre-training objectives for egocentric video datasets to learn generalizable representations of hand-object interactions and spatial-temporal dynamics.
- VLA Foundation Modeling: Develop and scale multi-modal Foundation Models that unify visual perception and natural language instructions into actionable robotic trajectories.
- Generative Policy Design: Design and optimize generative action heads using Diffusion Models and Flow-matching techniques to capture the multi-modal distribution of complex human movements.
- Humanoid Motion Alignment: Develop novel algorithms to align human-centric video representations with the kinematic constraints of 20+ DoF humanoid systems, ensuring fluid and stable execution.
- Reinforcement Learning & Fine-tuning: Utilize Offline RL and high-fidelity simulation fine-tuning to optimize foundation model performance for high-success-rate physical manipulation.
- Cross-Functional Research: Translate cutting-edge research in scaling laws and world models into production-ready architectures that enhance robot reliability and autonomy.
Requirements- PhD or Master's degree in Robotics, Machine Learning, or a closely related field, with a strong focus on data-driven manipulation, egocentric vision, or foundation models.
- Experience with Humanoid or Dexterous Manipulation, including a deep understanding of contact-rich physics.
- Excellent knowledge of Python, PyTorch, and the distributed training of large-scale neural networks (FSDP, NCCL).
- Proven expertise in Diffusion Models, Flow Matching, and Transformers.
- Hands-on experience deploying learning-based controllers on real robot hardware.
- Experience with Reinforcement Learning and simulation environments (e.g., IsaacLab, MuJoCo)
Benefits- Competitive compensation
- Enhanced pension plan
- Enhanced holiday & paid leave perks
- Relocation & permit sponsorship
- Central Zürich office with top-tier robotics testing facilities and infrastructure
- Joining Europe's leading robotics team & exposure to never-done-before research
- Energetic, collaborative culture with a bias for action and regular community events
