Robotics Applied Scientist
Field AI
Robotics Applied Scientist
Field AI
Builds AI that guides robots through changing sites
Investigate the appropriate representation for the robot's mission memory: a persistent spatio-temporal representation of what it saw, where, when, and how it changed, across missions and sites, so assets that move, disappear, or degrade are tracked rather than rediscovered.
Build world models that let the robot reason and plan over that memory and recover physical parameters through interaction for physics-aware digital twins.
Combine learned and physically grounded models, with geometry and dynamics as the ground truth.
Bridge the perception-reasoning gap for long-term autonomy.
PhD in robotics, computer science, electrical engineering, aerospace engineering, mechanical engineering, or a related field, with publications at top venues such as RSS, CoRL, ICRA, IROS, T-FR, T-RO, CVPR, NeurIPS, or similar, on scene understanding, semantic SLAM, lifelong or spatio-temporal mapping, world models, or embodied reasoning.
Hands-on experience with vision-language or foundation models applied to spatial reasoning, memory, or long-horizon embodied question answering.
Depth in 3D representations: scene graphs, neural or Gaussian-splat maps, or object-centric maps, and the estimation underneath them.
Strong Python; able to run your methods on real robots with C++ and ROS 2.
Clear thinking about what a representation is for and how to evaluate it, and comfort with a fast-paced environment where research questions are set by what the next deployment needs.
Change detection or long-term mapping results from real deployments.
Real2Sim, system identification through interaction, or digital-twin work.
Experience with behavior trees or task planning driven by language models.
Industrial or field robotics exposure.
Field AI builds autonomy software that lets mobile robots inspect and work in changing environments, including construction sites and industrial facilities. Its Field Foundation Models act as a shared control system for different machines, bringing perception, navigation and task planning together on the robot. Cameras and other sensors feed the system, while the platform sends site data into customers' project tools to track progress, record conditions and identify deviations from plans. The company sells commercial autonomy solutions and deployment services, integrating its software with robot manufacturers' equipment. Field AI was founded in 2023 and is headquartered in Irvine, California.
Explore more opportunities at Field AI