Dexterous Manipulation Research Engineer for Humanoids

Roboticsconnected; Zürich; On-site; CHF 150,000 - 230,000; Full time

Benefits offered by this job

Competitive compensation

Enhanced pension plan

Enhanced holiday & paid leave perks

Relocation & permit sponsorship

Central Zürich office with robotics测试

Job summary

Flexion in Zürich is hiring an AI Research Engineer to lead dexterous manipulation through egocentric video-based Foundation Models. You will build VLA models that translate observed human actions into 20+ DoF robot policies and deploy controllers on real hardware in unstructured settings.

The role emphasizes large-scale pre-training, multi-modal integration, and offline RL with simulation for robust robotic manipulation. Collaboration across AI engineering is essential.

Qualifications

• PhD or Master’s degree in Robotics, ML, or closely related field with a 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 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)

Responsibilities

• Architect and implement large-scale pre-training objectives for egocentric video datasets to learn generalizable representations of hand-object interactions and spatial-temporal dynamics.
• Develop and scale multi-modal Foundation Models that unify visual perception and natural language instructions into actionable robotic trajectories.
• Design and optimize generative action heads using Diffusion Models and Flow-matching techniques to capture the multi-modal distribution of complex human movements.
• Develop novel algorithms to align human-centric video representations with the kinematic constraints of 20+ DoF humanoid systems.
• Utilize Offline RL and high-fidelity simulation fine-tuning to optimize foundation model performance for high-success-rate physical manipulation.
• Translate cutting-edge research in scaling laws and world models into production-ready architectures that enhance robot reliability and autonomy.

Skills

Python

PyTorch

Distributed training

Reinforcement Learning

Simulation environments

Education

PhD or Master’s in Robotics or Machine Learning

Tools

Diffusion Models

Flow Matching

Transformers

NCCL

FSDP

Job description

Flexion in Zürich is hiring an AI Research Engineer to lead dexterous manipulation through egocentric video-based Foundation Models. You will build VLA models that translate observed human actions into 20+ DoF robot policies and deploy controllers on real hardware in unstructured settings.

The role emphasizes large-scale pre-training, multi-modal integration, and offline RL with simulation for robust robotic manipulation. Collaboration across AI engineering is essential.