Job Description

Hedra is building a world-class Physical AI research team to push the boundaries of action-conditioned world models and generative AI for physical systems. As a Researcher, you will drive original research into the intersection of generative modeling, embodied AI, and real-world physical applications alongside industrial partners. You will have access to large-scale compute, the freedom to pursue high-impact research directions, and a direct path to publication at top venues. We are looking for researchers who are excited to go beyond benchmarks and build models that operate in the real world — drawing on Hedra's leadership in generative modeling and the depth of our academic partnerships, including connections to Fei-Fei Li and the Stanford Vision & Learning Lab.

Responsibilities:

• Define and lead research directions in action-conditioned world models, physical AI, and generative modeling for embodied systems

• Design novel architectures, training objectives, and evaluation frameworks for VLMs, VLAs, and world models

• Direct research efforts with the goal of publishing in top journals.

• Partner with industrial collaborators to ground research in real-world physical AI use cases

• Mentor research engineers and collaborate cross-functionally to move research into production

• Stay at the frontier of the field — synthesizing relevant literature and identifying opportunities for impactful contributions

• Contribute to Hedra's research culture and external scientific reputation

Qualifications:

• PhD in Machine Learning, Computer Science, Robotics, or a related field, with publications at top ML or robotics venues

• Deep expertise in generative modeling, world models, or vision-language(-action) models

• Strong publication record at NeurIPS, ICML, ICLR, CVPR, CoRL, or equivalent venues

• Experience with large-scale model training and modern deep learning infrastructure

• Ability to independently drive research projects from ideation through publication

• Background in embodied AI, robotic manipulation, or sim-to-real transfer is highly desirable

• Experience with RLHF, DPO, or preference optimization for model alignment is a plus

• Strong collaboration and communication skills — comfortable bridging research and applied teams

Benefits:

• 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.