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
Skills Required
- PhD in robotics, computer science, electrical engineering, aerospace engineering, mechanical engineering, or a related field
- Publications at top venues such as RSS, CoRL, ICRA, IROS, T-FR, T-RO, CVPR, or NeurIPS
- Research experience in active perception, informative path planning, vision-language models, or semantic mapping for exploration
- Hands-on experience with vision-language or foundation models for robot perception
- Fluency in Python
- Ability to run methods on real robots using C++ and ROS 2
- Fluency with Bayesian or information-theoretic planning, calibration, uncertainty, and conformal methods
- Real-robot experience in field environments
- Experience with metric-semantic scene graphs, open-vocabulary mapping, or spatio-temporal scene representations
- Experience fine-tuning or deploying compact vision-language models on edge computers
- Exposure to robotic industrial inspection workflows
What We Do
FieldAI is pioneering the development of a field-proven, hardware agnostic brain technology that enables many different types of robots to operate autonomously in hazardous, offroad, and potentially harsh industrial settings – all without GPS, maps, or any pre-programmed routes.
