AI Research Engineer - Human Data

Flexion; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Competitive compensation

Enhanced pension plan

Relocation & permit sponsorship

Central Zürich office with top-tierRob

Joining Europe's leading robotics team

Energetic, collaborative culture witha

Job summary

Flexion in San Francisco seeks a Research Scientist/Engineer to advance generalizable manipulation using video demonstrations, wearable data, and multimodal models.

You will own end-to-end experimentation, build scalable capture pipelines, and translate cutting-edge research into robust, real-world robotic systems. PhD required; strong ML, RL/Imitation, and simulators experience preferred.

Qualifications

• PhD or equivalent practical experience in CS or related field.
• 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

• Scalable Human Data Integration: develop pipelines to integrate video demonstrations and wearable data into foundation model and RL workflows.
• 3D Object & Interaction Reconstruction: build pipelines to reconstruct manipulated 3D objects and environments from capture data.
• Simulation-to-Real & Reinforcement Learning: build simulation environments with reconstructed assets for dexterous manipulation policies.
• Multimodal Policy Architecture & Evaluation: design and evaluate models translating multi-sensor data into robust control.
• Embodied Transfer: design algorithms bridging human embodiment to dexterous robot hands and humanoid platforms.

Skills

Vision and multimodal models

Reinforcement learning

Imitation learning

Dataset design

Capture methodologies

Robotics simulators

Education

PhD in Computer Science or related field

Tools

Robotics simulators

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 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)

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
• 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

Zurich

• Enhanced pension plan
• Relocation & permit sponsorship
• Enhanced holiday & paid leave perks
• Central Zürich office with top-tier robotics testing facilities and infrastructure

San Franciso

• 401(k) with company contributions
• Health, dental & vision coverage with the flexibility to choose your own plan
• Open PTO policy & paid company holidays