FieldAI is transforming how robots interact with the real world. We build risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.
In Pittsburgh, we’re pushing the frontier of embodied intelligence by designing robot learning systems that scale across tasks, environments, and robot embodiments. We work on robotics foundation models, from vision and language to control, and we deploy what we build on real robots solving real problems in unstructured, real-world settings.
We are looking for a Robotics Research Engineer to build intelligent robotic systems that operate robustly in complex, unstructured real-world environments. This role sits at the intersection of robotics systems engineering and modern AI/ML, with a strong emphasis on deploying learning-enabled autonomy on real robots.
You will work across perception, planning, control, and hardware–software integration, while also developing and integrating machine-learning models (e.g., large-scale perception models, robot learning policies, multimodal and language-conditioned systems) as core components of deployed robotic systems.
Success in this role is defined by the ability to connect learning algorithms to physical robots, reason through real-world constraints, and iterate from research ideas to working fielded systems.