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VLA/WAM Integration & Deployment Engineer

Flexion Robotics · Zürich

New
Mid 🇬🇧 English
Python PyTorch TensorRT torch.compile VLA VLM Reinforcement learning

Job description

About the role

Flexion builds the intelligence layer behind the next generation of humanoid robots, founded by scientists in robot reinforcement learning and backed by international VC firms. As VLA/WAM Integration and Deployment specialist, you turn VLA and World-Action Model research into reliable capabilities running on real humanoid robots. You work across the full deployment loop: integrating foundation models with agentic systems, optimizing inference, collecting targeted real-world data, running principled evaluations and feeding deployment experience back into training.

Key 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 workflows for teleoperation, interventions, autonomous rollouts, failures, recoveries and targeted post-training data collection
  • Build reproducible real-robot evaluations, regression suites, failure analysis and comparisons between model versions
  • Turn real-world successes and failures into datasets and experiments that improve subsequent pre-training and post-training

Required profile

  • Experience deploying learning-based models in robotics, autonomous systems or another real-time embodied setting
  • Comfortable working directly with robots, sensors, data collection and imperfect real-world hardware
  • Ability to work across model training, deployment, data, infrastructure and integration

Required skills

  • Strong Python and PyTorch skills
  • Familiarity with VLA, VLM, diffusion, flow-matching, video/world-model or related multimodal architectures
  • Systems intuition for inference latency, model serving, data pipelines, logging and debugging complex ML systems
  • Experience designing evaluations and diagnosing failures in learned systems
  • Bonus: agentic AI, TensorRT, torch.compile, quantization, reinforcement learning, sim-to-real, teleoperation and human-in-the-loop learning

What we offer

  • Competitive compensation
  • Enhanced pension plan and enhanced holiday and paid leave perks
  • Relocation and permit sponsorship
  • Central Zurich office with robotics testing facilities
  • For San Francisco: 401(k) with company contributions, health, dental and vision coverage, open PTO and paid company holidays

Questions fréquentes

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Source : ats:workable

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Published 20 minutes ago

Expires 1 month from now

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Flexion Robotics

Zürich