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About the Role

As our VLA/WAM Integration and Deployment specialist, you will turn our VLA and World-Action Model research into reliable capabilities running on real humanoid robots. This is a broad, hands-on role for someone who enjoys making advanced models work as part of a complete autonomous system rather than optimizing a single research component in isolation.

Responsibilities

Deploy and validate learned policies across multiple humanoid platforms and real-world tasks.
Optimize model serving, latency, action generation, asynchronous execution, and closed-loop control for real-time robot operation.
Connect VLA/WAM policies with higher-level agents for task decomposition, policy selection, structured conditioning, verification, recovery, and replanning.
Develop improved workflows for teleoperation, interventions, autonomous rollouts, failures, recoveries, and targeted post-training data collection.
Develop reproducible real-robot evaluations, regression suites, failure analysis, and principled comparisons between model versions.
Turn real-world successes and failures into datasets and experiments that improve subsequent pre-training and post-training.

Requirements

Strong Python and PyTorch skills and experience working with modern deep-learning systems.
Experience deploying learning-based models in robotics, autonomous systems, or another real-time embodied setting.
Familiarity with VLA, VLM, diffusion, flow-matching, video/world-model, or related multimodal architectures.
Strong systems intuition around inference latency, model serving, data pipelines, logging, and debugging complex ML systems.
Experience designing rigorous evaluation and diagnosing failures in learned systems.
Comfort working directly with robots, sensors, data collection, and imperfect real-world hardware.
Ability to work across model training, deployment, data, infrastructure, and integration rather than within a narrowly defined research area.
Experience with agentic AI systems, tool use, hierarchical policies, planning, or closed-loop verification.
Experience with TensorRT, torch.compile, quantization, model compression, or other inference optimization techniques.
Experience with teleoperation, human-in-the-loop learning, autonomous data collection, or online/post-training workflows.
Experience with humanoids, dexterous manipulation, loco-manipulation, or whole-body control.
Familiarity with reinforcement learning, continual learning, and sim-to-real.

Location

Zürich
San Francisco

Salary/Compensations

Competitive compensation

Benefits

Enhanced pension plan
Enhanced holiday & paid leave perks
Relocation & permit sponsorship
Joining Europe's leading robotics team & exposure to never-done-before research
Energetic, collaborative culture with a bias for action and regular community events
401(k) with company contributions
Health, dental & vision coverage with the flexibility to choose your own plan
Open PTO policy & paid company holidays

About the Company

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.