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Flexion Robotics

Humanoid-robotics AI brain for autonomy

VLA/WAM Integration & Deployment Engineer

Robotics & Autonomous SystemsConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USANo salary listed

About the Job

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 machine-learning 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.

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.

Desired Qualifications

  • 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.

About the Company

Flexion Robotics

Humanoid-robotics AI brain for autonomy

Flexion Robotics builds the AI brain for humanoid robots, enabling autonomy by letting robots understand natural-language tasks, plan motions, and control their bodies through reinforcement learning and sim-to-real training. Its stack includes a command layer that uses language models for task decomposition, a motion layer with vision-language-action models trained largely on synthetic data, and a low-latency whole-body control layer with modular skills. The company differentiates itself with an ETH Zurich–trained engineering team and a full-stack approach that covers language, motion, and real-time control, plus active funding and OEM partnerships. Its goal is to enable useful, widespread autonomy for humanoid robots by partnering with original equipment manufacturers to scale robot fleets and establish a US presence.

Company size
51-200
Company stage
Series A
Total funding
$57.4M
Headquarters
Zurich, Switzerland
Founded
2025

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