Field AI is transforming how robots interact with the real world by building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence.
Requirements
- Strong interest in robotics, computer vision, 3D reconstruction, and simulation.
- Experience with camera data (mono and stereo), LiDAR data, or multimodal perception.
- Familiarity with Gaussian Splatting, NeRFs, or related neural reconstruction approaches.
- Working knowledge of Isaac Sim or other simulation frameworks.
- Comfortable programming in Python (C++ and ROS/ROS2 experience is a plus, not required).
- Ability to run experiments, work with datasets, and iterate quickly in an application-focused project.
- Experience with vision transformers, depth estimation, or domain adaptation.
Responsibilities
- Work with real robot camera and LiDAR datasets to test end-to-end reconstruction pipelines.
- Apply techniques such as Gaussian Splatting, NeRF-style reconstruction, and vision transformer–based models to create realistic scene representations.
- Develop real-to-sim workflows that convert raw sensor data into USD environments for simulation platforms, such as Isaac Sim.
- Experiment with NVIDIA tools and modern GPU workflows to validate reconstruction quality and simulation utility.
- Collaborate with autonomy and hardware teams to align reconstructed environments with real robot behavior and field constraints.
Other
- Ability to start immediately or no later than January 2026.
- Work authorization that permits starting immediately.
- Bachelor's, Master's, or Ph.D. degree (not explicitly mentioned but implied).
- Ability to work in a hybrid or remote environment.
- Strong communication and collaboration skills (implied but not explicitly mentioned).
