# AI Research Engineer - Dexterous Manipulation (Egocentric Models)

**Company:** [Flexion Robotics](http://jobs.workable.com/companies/gVBzUN7bFNioWR2m4ydNgi.md)
**Location:** Zürich, Switzerland
**Workplace:** on site
**Department:** AI Engineering

[Apply for this job](http://jobs.workable.com/view/d1bcf635-152f-40c8-a38f-75b79382c2fd)

## 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.

**The role**

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