Flexion Robotics

Humanoid-robotics AI brain for autonomy

VLA/WAM Research Engineer

AI ResearchConfirmed live in the last 24 hours

Full-timeSan Francisco, CA, USANo salary listed

About the Job

Requirements

Strong Python and PyTorch skills, with hands-on experience developing and training modern deep-learning models.
Research or engineering experience in one or more of the following: VLA/VLM architectures, multimodal foundation models, world models, large-scale pretraining, robot learning, or reinforcement learning/post-training.
Experience owning substantial model development or training experiments, including architecture choices, training objectives, optimization, and evaluation.
Solid understanding of modern deep-learning architectures, representation learning, and the relationship between data, training objectives, and model performance.
Ability to design rigorous experiments, diagnose training failures, and use empirical results to guide research decisions.
Familiarity with large-scale datasets, model training workflows, and the challenges of generalization across tasks, environments, or embodiments.
Ability to critically evaluate recent research and translate promising ideas into working models and experiments.
Comfort working across model architecture, training, and adaptation while bringing particular expertise in at least one of these areas.

Responsibilities

Design and develop multimodal foundation models combining vision, language, robot state, actions, and temporal context for general-purpose robotic control.
Develop and execute large-scale pretraining across diverse robot embodiments, teleoperation, egocentric human data, and simulated experience.
Improve data mixtures, training objectives, optimization strategies, and distributed training workflows to deliver increasingly capable foundation models on a regular cadence.
Develop predictive world and action models for representation learning, future-state prediction, planning, and robot policy improvement.
Develop methods to efficiently adapt pretrained models to new tasks, environments, embodiments, and capabilities.
Improve models using real-world and simulated robot experience, including imitation learning, reinforcement learning, reward/value-guided learning, and specialist-to-generalist distillation.
Develop methods to learn from autonomous rollouts, failures, interventions, and recoveries while retaining existing capabilities and avoiding catastrophic forgetting.
Design rigorous experiments, evaluate models in simulation and on real robots, and use results to guide architecture and training decisions.

Desired Qualifications

Experience training VLA, VLM, diffusion, flow-matching, video/world models, or other multimodal generative architectures.
Experience with distributed training, multi-node GPU systems, large-scale data mixtures, and foundation-model training optimization.
Experience with reinforcement learning, offline reinforcement learning, reward/value learning, specialist distillation, or continual learning.
Experience with predictive world models, video generation, latent dynamics, or self-supervised representation learning.
Experience with multi-embodiment robot learning, action representations, or adapting policies across different robot platforms.
Experience working with teleoperation, egocentric video, simulation, autonomous rollouts, or other large-scale embodied datasets.
Experience evaluating learned policies on real robots, particularly humanoids or dexterous manipulation systems.

VLA/WAM Research Engineer

Flexion Robotics · San Francisco, United States · Full-time · Posted 2026-11-03

Workplace: on_site

Department: AI Engineering

Description

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 Research Engineer, you'll develop the foundation models that power our humanoid robots, spanning model architecture, large-scale pre-training, adaptation, and post-training.

You will work across the model lifecycle: designing VLA and World-Action Model architectures, training foundation models on large and diverse embodied datasets, developing methods for efficient adaptation to new tasks and embodiments, and using robot experience to continuously improve model capabilities.

In particular, we are looking for researchers and engineers with deep experience in one or more of the following areas: VLA/VLM or multimodal model architecture, large-scale foundation-model pre-training, world models and predictive learning, robot learning, or post-training and continual learning. If you have strong depth in one of these areas and are excited to work across the broader problem of building general-purpose robot foundation models, you will be a strong fit for the role.

Key responsibilities

VLA/WAM Architecture: Design and develop multimodal foundation models combining vision, language, robot state, actions, and temporal context for general-purpose robotic control.
Foundation Model Pretraining: Develop and execute large-scale pretraining across diverse robot embodiments, teleoperation, egocentric human data, and simulated experience.
Training Recipes & Scaling: Improve data mixtures, training objectives, optimization strategies, and distributed training workflows to deliver increasingly capable foundation models on a regular cadence.
World-Action Models: Develop predictive world and action models for representation learning, future-state prediction, planning, and robot policy improvement.
Adaptation & Mid-training: Develop methods to efficiently adapt pretrained models to new tasks, environments, embodiments, and capabilities.
Post-training & Reinforcement Learning: Improve models using real-world and simulated robot experience, including imitation learning, RL, reward/value-guided learning, and specialist-to-generalist distillation.
Continual Learning: Develop methods to learn from autonomous rollouts, failures, interventions, and recoveries while retaining existing capabilities and avoiding catastrophic forgetting.
Evaluation & Research: Design rigorous experiments, evaluate models in simulation and on real robots, and use results to guide architecture and training decisions.

Requirements

Strong Python and PyTorch skills, with hands-on experience developing and training modern deep-learning models.
Research or engineering experience in one or more of the following: VLA/VLM architectures, multimodal foundation models, world models, large-scale pretraining, robot learning, or RL/post-training.
Experience owning substantial model development or training experiments, including architecture choices, training objectives, optimization, and evaluation.
Solid understanding of modern deep-learning architectures, representation learning, and the relationship between data, training objectives, and model performance.
Ability to design rigorous experiments, diagnose training failures, and use empirical results to guide research decisions.
Familiarity with large-scale datasets, model training workflows, and the challenges of generalization across tasks, environments, or embodiments.
Ability to critically evaluate recent research and translate promising ideas into working models and experiments.
Comfortable working across model architecture, training, and adaptation while bringing particular expertise in at least one of these areas.

Additionally, the following are a plus but not required:

Experience training VLA, VLM, diffusion, flow-matching, video/world models, or other multimodal generative architectures.
Experience with distributed training, multi-node GPU systems, large-scale data mixtures, and foundation-model training optimization.
Experience with reinforcement learning, offline RL, reward/value learning, specialist distillation, or continual learning.
Experience with predictive world models, video generation, latent dynamics, or self-supervised representation learning.
Experience with multi-embodiment robot learning, action representations, or adapting policies across different robot platforms.
Experience working with teleoperation, egocentric video, simulation, autonomous rollouts, or other large-scale embodied datasets.
Experience evaluating learned policies on real robots, particularly humanoids or dexterous manipulation systems.

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 Franciso

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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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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