Research Engineer

Hedra•San Francisco, CA

22h

About The Position

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.

Requirements

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
BS/MS/PhD in Computer Science, Machine Learning, Robotics, or a related field

Nice To Haves

Background in robotics, embodied AI, or sim-to-real transfer is a plus
Experience with video understanding or temporal reasoning is a plus

Responsibilities

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 video, sensory inputs, and action sequences
Evaluate model performance using both benchmark datasets and real-world deployment metrics
Contributions research publications a plus
Collaborate with industrial partners to adapt generative models for real-world physical AI applications