physical-ai - applied research ( CTO team)

our CTO group is looking for an outstanding physical-ai applied researcher to join our team.
the CTO group is a small, elite research unit shaping the next generation of algorithmic foundations behind our autonomous driving systems. the group operates at the core of the decision-making and planning stack, addressing some of the most challenging problems in real-world autonomy. we are seeking a researcher who thrives at the intersection of Machine Learning, decision-making, and algorithmic rigor - someone who is excited about advancing learning-based approaches for safety-critical, large-scale physical systems. in this role, you will develop novel approaches for planning and decision-making in interactive, multi-agent driving environments. you will combine deep & reinforcement learning with classical algorithmic structure and formal reasoning. the problems are open-ended, scientifically challenging, and deployed at unprecedented scale. this is a rare opportunity to conduct high-impact applied research, taking ideas from theory and papers into real-world autonomous systems at scale. if youre excited about pushing the boundaries of learning-based decision-making, wed love you to join us and help shape the future of physical ai at our company. what will your job look like:
design and develop novel learning-based algorithms for decision-making and planning in complex physical environments.
advance model architectures for long-horizon reasoning, multi-agent interaction, and uncertainty-aware prediction.
integrate deep learning components into structured planning pipelines with clear formal objectives and safety constraints.
formulate problems mathematically and derive principled learning objectives grounded in real-world system requirements.
lead research directions from conception to full-scale production.
develop using Python (pytorch or similar frameworks) as well as C ++/gpu/cuda.