Robotics Applied Scientist, Spatio-Temporal Scene Understanding & Memory
Required skills
- robotics
- applied scientist
- spatio-temporal scene understanding
- memory
- embodied AI
- robot deployments
- AI systems
- learning systems
- electrical engineering
- aerospace engineering
Nice to have
- change detection
- long-term mapping
- real deployments
- Real2Sim
- system identification
- digital-twin
Role summary
FieldAI is seeking a Robotics Applied Scientist in Irvine, CA, to focus on Spatio-Temporal Scene Understanding & Memory for embodied AI systems. This role involves developing persistent memory representations for robots, building world models for reasoning and planning, and integrating learned and physically grounded models. The ideal candidate will have a PhD in a related field with publications in top venues and hands-on experience with foundation models, 3D representations, Python, C++, and ROS 2. Preferred qualifications include experience with real-world deployments, digital twins, and behavior trees. This is an onsite position focused on shipping AI systems for real-world robotic applications.
What You Will Get To Do
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.
What You Have
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.
What Will Set You Apart
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.
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