Research Scientist

Overview:

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.