Research Engineer/Scientist - Dexterous Manipulation

Flexion Robotics; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Competitive compensation

Enhanced pension plan

Enhanced holiday & paid leave perks

Relocation sponsorship

Central Zürich office facilities

Job summary

Flexion is seeking an expert in dexterous manipulation to lead the development of physical foundation models using egocentric video to enable humanoid robots to interact with the world with human-like fluidity. You will own model architecture, integrate egocentric priors with real-time robot policies, and ensure reliable manipulation in unstructured environments.

The role requires PhD or Master’s in Robotics/ML, hands-on experience with Humanoid or Dexterous Manipulation, and deep knowledge of

Qualifications

• PhD or Master’s in Robotics or ML with focus on data-driven manipulation, egocentric vision, or foundation models.
• Experience with Humanoid or Dexterous Manipulation and contact-rich physics.
• Strong Python, PyTorch, and large-scale training knowledge (FSDP, NCCL).
• Expertise in Diffusion Models, Flow Matching, and Transformers.

Responsibilities

• Architect and implement scalable egocentric pre-training objectives for hand-object interactions.
• Develop and scale Vision-Language-Action foundation models for robotic trajectories.
• Design and optimize generative action heads using diffusion and flow-matching techniques.
• Align human-centric video representations with 20+ DoF humanoid constraints for stable control.
• Utilize offline RL and high-fidelity simulation to refine model performance.
• Translate cutting-edge research into production-ready architectures for reliability.

Skills

Robotics research

Hands-on experimentation

Problem solving

Team collaboration

Education

PhD or Master's in Robotics or ML

Tools

Python

PyTorch

Diffusion Models

Flow Matching

Transformers

MuJoCo

IsaacGym

Distributed training (FSDP, NCCL)

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.

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.

• 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)

• 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