# VLA/WAM Integration & Deployment Engineer at Flexion Robotics

> United States · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/workable%3Aflexion-robotics%3A60F67D4D1C (read 2026-11-11).

## Facts (observed)

- Company: [Flexion Robotics](https://alderhq.app/companies/flexion-robotics)
- Location text: San Francisco, California, United States
- Where it can be done from (inferred): United States (evidence: "Location: San Francisco, California, United States")
- Posted: 2026-11-05 (6 days ago)
- First seen by alder: 2026-11-11 · last verified: 2026-11-11
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://apply.workable.com/j/60F67D4D1C
- Skills listed (inferred): Python, PyTorch

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 2119): p25 $162k, median $200k, p75 $240k

## Description (the employer's text)

### 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

As our VLA/WAM Integration and Deployment specialist you'll turn our VLA and World-Action Model research into reliable capabilities running on real humanoid robots.

You will 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.

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.

### Key responsibilities

- VLA/WAM Deployment: Deploy and validate learned policies across multiple humanoid platforms and real-world tasks.
- Fast Inference: Optimize model serving, latency, action generation, asynchronous execution, and closed-loop control for real-time robot operation.
- Agentic Integration: Connect VLA/WAM policies with higher-level agents for task decomposition, policy selection, structured conditioning, verification, recovery, and replanning.
- In-Situ Data Collection: Develop improved workflows for teleoperation, interventions, autonomous rollouts, failures, recoveries, and targeted post-training data collection.
- Evaluation & Testing: Develop reproducible real-robot evaluations, regression suites, failure analysis, and principled comparisons between model versions.
- Deployment-to-Training Loop: 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.
### Bonus

- 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.
### 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 Francisco

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

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/workable%3Aflexion-robotics%3A60F67D4D1C and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.