Centific's Physical AI Lab seeks a Principal Research Scientist to lead research in multimodal foundation models, vision AI, and embodied intelligence. The role involves driving research, mentoring a team, and contributing to model design and large-scale training for robotics and autonomous systems.
Responsibilities
Lead high-impact research in multimodal foundation models, world models, embodied AI, vision-language-action systems, and agentic AI.
Develop new approaches for perception, temporal reasoning, spatial intelligence, affordance understanding, and sim2real transfer.
Advance robotics capabilities including dexterous manipulation, contact-rich interaction, and long-horizon task execution.
Contribute to large-scale model building, including multimodal pretraining, distributed training, fine-tuning, distillation, and evaluation.
Guide integration of research with simulation platforms such as Isaac Sim, MuJoCo, and Omniverse.
Establish rigorous benchmarks and reproducible evaluation frameworks for robustness, safety, and real-world deployment.
Mentor Ph.D. interns and engineers, building a strong research culture.
Requirements
Ph.D. in Computer Science, Robotics, Machine Learning, Computer Vision, Autonomous Systems, or related field.
Strong publication record in top venues such as CVPR, NeurIPS, ICLR, CoRL, RSS, etc.
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5+ years of research experience in academia, industry, or advanced R&D environments.
Demonstrated experience building or advancing large-scale foundational models, novel architectures, or training methods in multimodal AI, vision, robotics, autonomous driving, embodied AI, world models, or simulation-based learning.
Deep expertise in PyTorch and/or JAX, GPU training, distributed experimentation, and large-scale model development.
Proven ability to lead ambitious technical programs and mentor junior researchers.
Nice to Have
Publications or patents in multimodal foundation models, dexterous robotics, autonomous driving, spatial intelligence, simulation-based learning, manipulation, or embodied AI.
Strong experience in Vision AI including perception, tracking, 3D scene understanding, video understanding, sensor fusion, or multimodal reasoning.
Familiarity with agentic AI systems, tool-using agents, planning frameworks, and memory-based architectures.
Experience with Isaac Sim, MuJoCo, OpenUSD/Omniverse, Open3D, PyTorch3D, NeRF/3DGS, or related simulation and 3D stacks.
Familiarity with imitation learning, reinforcement learning, planning, MPC, control, or policy learning for robotics.
Experience with Ray, Kubernetes, Triton, TensorRT, Docker, W&B, or large-scale training infrastructure.
Background in trustworthy AI, robotics safety, evaluation, or explainability for autonomous systems.
