Hedra builds models that generate, perceive, and predict the visual world — and the platform that runs them in production. Backed by a16z and a16z speedrun.
About the role
Hedra is a pioneering generative modeling company — first models to market — now building a Physical AI team to bring these models to real-world industry and economy use cases. As a Research Engineer on our Physical AI team, you will lead pre-training and post-training on action-conditioned world models, working hand-in-hand with industrial partners to close the loop between generative AI and physical systems.
What they're looking for
- Experience with pre-training or post-training on large generative models (video, multimodal, or action-conditioned)
- Hands-on proficiency with PyTorch and distributed training frameworks (FSDP, DeepSpeed)
- Strong fundamentals in machine learning, optimization, and large-scale data processing
- Familiarity with VLMs, VLAs, or world models
- Background in robotics, embodied AI, or sim-to-real transfer is a plus
- Experience with video understanding or temporal reasoning is a plus
More about this role
Hedra is a pioneering generative modeling company — first models to market — now building a Physical AI team to bring these models to real-world industry and economy use cases. As a Research Engineer on our Physical AI team, you will lead pre-training and post-training on action-conditioned world models, working hand-in-hand with industrial partners to close the loop between generative AI and physical systems. This is not a black-box applied role: your work will be published, your infrastructure will be serious, and your impact will be direct. If you want to work at the frontier of generative modeling and physical AI, this is the team.
Design, implement, and run pre-training and post-training pipelines for action-conditioned world models and vision-language-action (VLA) models
Develop and refine training methodologies, including fine-tuning, reinforcement learning, and large-scale multimodal learning
Design and generate training and evaluation datasets from simulation, including environment setup, domain randomization, and sim-to-real transfer strategies
Build distributed training infrastructure using PyTorch, FSDP, and DeepSpeed
Work with multimodal data pipelines involving...
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