VLA/WAM Research Engineer

Flexion Robotics•San Francisco, CA

About The Position

At Flexion, the VLA/WAM Research Engineer develops foundation models that power humanoid robots, spanning model architecture, large-scale pretraining, adaptation, and post-training. The role covers the model lifecycle: designing VLA and World-Action Model architectures, training foundation models on large and diverse embodied datasets, developing efficient adaptation methods for new tasks and embodiments, and using robot experience to improve model capabilities. The role is suited to researchers and engineers with depth in one or more areas such as VLA/VLM or multimodal model architecture, large-scale foundation-model pretraining, world models and predictive learning, robot learning, or post-training and continual learning, who are also interested in working across general-purpose robot foundation models. Flexion describes itself as building the intelligence layer for the next generation of humanoid robots, with a mission to accelerate the transition from fragile prototypes to real-world humanoid deployment. The company was founded by scientists in robot reinforcement learning and is backed by international venture capital firms.

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: 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.

Nice To Haves

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.

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 regularly.
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.

Benefits

Competitive compensation.
Enhanced pension plan.
Enhanced holiday and paid leave perks.
Relocation and permit sponsorship.
Central Zürich office with robotics testing facilities and infrastructure.
Exposure to robotics research as part of a leading robotics team.
Energetic, collaborative culture with regular community events.
For San Francisco: 401(k) with company contributions.
For San Francisco: Health, dental, and vision coverage, with flexibility to choose a plan.
For San Francisco: Open PTO policy and paid company holidays.