Join Hedra as a Research Engineer to lead the development of action-conditioned world models in a pioneering Physical AI team.
Posted by employer 5 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
AI in the day-to-day
Your work will be published, and you will collaborate with industrial partners to adapt generative models for real-world applications.
Requirements
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Benefits
Joblaze summary
In the role of Research Engineer at Hedra, the individual will focus on designing and implementing training pipelines for action-conditioned world models, collaborating closely with industrial partners to apply generative AI in practical settings. Key skills include proficiency in PyTorch and experience with distributed training frameworks, alongside a solid foundation in machine learning and data processing. This position is ideal for candidates with a background in robotics or embodied AI, particularly those looking to contribute to cutting-edge research and development in physical AI applications.
Joblaze insights
Quick facts
From the original posting
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 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
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
BS/MS/PhD in Computer Science, Machine Learning, Robotics, or a related field
Competitive compensation and equity
401k (no match)
Healthcare (Silver PPO Medical, Vision, Dental)
Lunch and snacks at the office
We encourage you to apply even if you don't fully meet all the listed requirements; we value potential and diverse perspectives, and your unique skills could be a great asset to our team.