Robotics Applied Scientist, Active Perception
Required skills
- active perception
- viewpoint planning
- vision-language reasoning
- semantic mapping
- exploration
- foundation models
- robot perception
- Python
- C++
- ROS 2
Nice to have
- real-robot experience
Role summary
FieldAI is seeking a Robotics Applied Scientist specializing in Active Perception to join their Irvine team. This role involves formulating active perception problems, developing viewpoint planning policies, and researching vision-language reasoning for robots. The scientist will own research and development from concept to real-world deployment, contribute to the technical roadmap, and mentor junior team members. Qualifications include a PhD in a related field, research experience in active perception or related areas, fluency in Python and C++, and a strong understanding of uncertainty principles. Experience with real robots, foundation models, and edge computing is highly desirable.
What You Will Get To Do
Formulate the active perception problem for inspection and a broad range of robot behaviors
Develop viewpoint planning policies for active information gathering
Research calibrated confidence and abstention for vision-language reasoning on the robot
Own the research and development work through to the real-world experiments that prove feasibility and deployability
Investigate and shape the technical roadmap, aligning real-world needs with technical development
Mentor junior team members and collaborators
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.
Research experience in active perception, informative path planning, vision-language models, or semantic mapping for exploration.
Hands-on work with vision-language or foundation models for robot perception, plus the physical principles governing them.
Fluency in Python and the ability to run your methods on real robots with C++ and ROS 2.
A principled fluency with uncertainty: Bayesian or information-theoretic planning, calibration, and conformal methods
What Will Set You Apart
Real-robot experience in the wild
Work on metric-semantic scene graphs, open-vocabulary mapping, or spatio-temporal scene representations.
Experience fine-tuning or deploying compact VLMs on edge computers
Exposure to robotic industrial inspection workflows
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