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# VLA/WAM Integration & Deployment Engineer

- **Company:** FLEXION INC
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Training Data, Software Debugging, Python (Programming Language), Model Compression, Software Deployment, Reinforcement Learning, Data Logging, Pytorch, Deep Learning, Agentic-AI, Discretization, Low Latency, Machine Learning Operations, TensorRT, Data Pipelines, Human in the Loop
- **Published:** November 6, 2026
- **Apply:** https://www.thejobnetwork.com/job/d5ea80bb-c993-4796-b5fd-db5b7ba6b26c/vlawam-integration-deployment-engineer

## About the Role

* 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.

## Description

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., * 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.