VLA/WAM Integration & Deployment Engineer

Flexion; San Francisco (CA); Presencial; USD 140.000 - 210.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive compensation

Pension plan

Holiday & leave perks

Relocation sponsorship

Central Zürich office

Leading robotics team

Energetic culture

Descripción de la vacante

Flexion in San Francisco is seeking a VLA/WAM Integration and Deployment specialist to turn research into reliable humanoid capabilities. You will deploy learned policies across humanoid platforms, optimize inference, collect data, and evaluate real-world performance.

You will work across model training, deployment, data pipelines, and robotics hardware, partnering with engineers to improve end-to-end system reliability in dynamic environments.

Formación

• Strong Python and PyTorch skills and experience 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.

Responsabilidades

• VLA/WAM Deployment: Deploy and validate learned policies across 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.

Conocimientos

Python

PyTorch

Robotics deployment

Real-time systems

Evaluation design

Robotic hardware

cross-disciplinary collaboration

Herramientas

TensorRT

Torch.compile

Quantization

Model compression

Descripción del empleo

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.

• 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.

• 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