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About the Role

Flexion is developing the intelligence layer for next-generation humanoid robots, aiming to accelerate their transition from prototypes to real-world deployment. Founded by leading scientists in robot reinforcement learning, the company is backed by prominent international VC firms and has rapidly achieved significant milestones in humanoid robot capabilities.

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, RL, 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.

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.

Skills

VLA/VLM architectures
Multimodal foundation models
World models
Large-scale pretraining
Robot learning
RL/post-training
Python
PyTorch
Deep learning model development
Deep learning model training
Representation learning
Large-scale datasets
Model training workflows
Generalization across tasks, environments, or embodiments
Research evaluation
VLA
VLM
Diffusion models
Flow-matching
Video models
Multimodal generative architectures
Distributed training
Multi-node GPU systems
Large-scale data mixtures
Foundation-model training optimization
Reinforcement learning
Offline RL
Reward/value learning
Specialist distillation
Continual learning
Predictive world models
Video generation
Latent dynamics
Self-supervised representation learning
Multi-embodiment robot learning
Action representations
Policy adaptation
Teleoperation
Egocentric video
Simulation
Autonomous rollouts
Embodied datasets
Policy evaluation on real robots
Humanoid robot systems
Dexterous manipulation systems

Location

Zürich
San Francisco

Work Type

Onsite

Salary/Compensations

Competitive compensation

Benefits

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

About the Company

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.