AI Research Engineer - Human Data - Robotics

Startupvalleys; Altburg; On-site; CHF 150,000 - 210,000; Full time

Benefits offered by this job

Competitive pay

Pension plan

Paid leave

Zurich office facilities

401(k) with company contributions

Health, dental & vision coverage

Job summary

Flexion Robotics in Zurich seeks an expert in robotics foundation models and multimodal human action data to advance dexterous manipulation. You will design capture pipelines, curate datasets, and build architectures that scale to real-world robotic systems.

The role blends research and engineering, turning cutting-edge ideas into robust, deployable hardware-enabled solutions within a fast-growing startup. Strong collaboration and hands-on experimentation are essential.

Qualifications

• PhD in Computer Science, a related field, or equivalent practical experience.
• Experience with vision, vision-language, video, and other multimodal models.
• Experience with multimodal generative modeling, training, and inference.
• Experience with reinforcement learning and imitation learning.

Responsibilities

• Develop methodologies to integrate human video demonstrations and wearable data into foundation model and RL workflows.
• Build automated pipelines to reconstruct 3D objects and environments for simulation.
• Create simulation environments with reconstructed assets and use RL to learn dexterous policies.
• Design, train, and evaluate robot foundation models translating multi-sensor data into control.
• Bridge human embodiment to dexterous hands and humanoid platforms.

Skills

Multimodal vision

Reinforcement learning

Imitation learning

Dataset design

Education

PhD in Computer Science or related field

Tools

Simulation environments

Robotics hardware & testing

Job description

Data & ML

Original posting — from Flexion Robotics Open original

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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 robotics foundation models and multi-modal human action data to advance the frontiers of generalizable manipulation. This position bridges the gap between human embodiment and robotic execution by leveraging video demonstrations, wearable capture systems (such as UMI or sensorized gloves), and complementary data sources. You bring a deep understanding of the full lifecycle required to scale dexterous capabilities, spanning the design and deployment of specialized capture devices, dataset curation, and algorithmic and architectural modeling.

As a Research Scientist/Engineer, you will drive large-scale experimentation and deploy breakthrough ideas across core physical intelligence problems. You will own the development of novel systems that turn cutting-edge research into robust, real-world implementations, ultimately pushing the limits of what our hardware can achieve.

Key responsibilities

Scalable Human Data Integration: Develop methodologies and capture pipelines to integrate human video demonstrations, wearable systems (such as UMI or sensorized gloves), and unconstrained manipulation data directly into foundation model and RL workflows.

3D Object & Interaction Reconstruction: Build automated pipelines to reconstruct manipulated 3D objects and environments from capture data, importing digital twin assets into simulation to enable scalable scenario synthesis.

Simulation-to-Real & Reinforcement Learning: Build simulation environments with reconstructed assets, leveraging reinforcement learning, tactile, and force feedback to produce dexterous manipulation policies.

Multimodal Policy Architecture & Evaluation: Design, train, and evaluate robot foundation models that translate multi-sensor data streams into robust control.

Embodied Transfer: Design algorithms to bridge human embodiment to high-DOF dexterous hands and humanoid platforms.

Requirements

PhD in Computer Science, a related field, or equivalent practical experience.

Experience with vision, vision-language, video, and other multimodal models, especially ones trained with human data.

Experience with multimodal generative modeling, training, and inference.

Experience with reinforcement learning and imitation learning.

Preferred qualifications:

Experience with capture methodologies, dataset design, experimentation, and incorporation of captured human action data into VLA, WAM, or RL training.

Experience with simulators and real-world robots, especially dexterous manipulation, as well as multimodal sensing (e.g., tactile, forces).

Competitive compensation

Enhanced pension plan

Enhanced holiday & paid leave perks

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

Competitive Compensation

Joining a leading robotics team & exposure to never-done-before research

Energetic, collaborative culture with a bias for action and regular community events

Enhanced pension plan

Enhanced holiday & paid leave perks

Central Zürich office with top-tier robotics testing facilities and infrastructure

401(k) with company contributions

Health, dental & vision coverage with the flexibility to choose your own plan

Shape the Future

About this startup

Flexion Robotics

Employees 11–50

Founded 2026

City CH