Hedra
Research Engineer
San Francisco
Sponsorship not specifiedDetected 98 days ago
Machine LearningPyTorchData EngineeringLLMsRoboticsResearch
About the role
- 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.
Responsibilities
- 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
- Collaborate with industrial partners to adapt generative models for real-world physical AI applications
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)
- Familiarity with VLMs, VLAs, or world models
- Strong fundamentals in machine learning, optimization, and large-scale data processing
- BS/MS/PhD in Computer Science, Machine Learning, Robotics, or a related field
Nice to have
- Contributions research publications a plus
- Background in robotics, embodied AI, or sim-to-real transfer is a plus
- Experience with video understanding or temporal reasoning is a plus
Compensation
- Competitive compensation and equity
Benefits
- Competitive compensation and equity
- Healthcare (Silver PPO Medical, Vision, Dental)
- 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
Company info
- 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.
This listing is sourced directly from Hedra's careers page and normalized into a canonical job model.