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.