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.