About Us
Field AI builds field-proven embodied AI that enables robots to operate autonomously in complex, unstructured real-world environments. Our systems perceive, reason, and act directly on the robot, running on edge hardware under strict constraints on latency, power, and reliability.
We focus on translating cutting-edge AI research into deployable, production-grade autonomy, with an emphasis on robustness, efficiency, and real-world performance. Our AI stack runs on embedded platforms such as NVIDIA Jetson and Orin, powering robots that operate continuously without reliance on cloud compute or curated environments.
What You’ll Do:
Convert and optimize 2D/3D CNNs and Transformer-based models (PyTorch/TensorFlow → ONNX → TensorRT/Triton) for real-time inference on Jetson/Orin platforms.Apply model compression techniques—quantization, pruning, distillation, weight sharing—to meet strict constraints on latency, memory, bandwidth, and power.Develop custom TensorRT plugins and CUDA kernels for performance-critical components.Integrate optimized models into the broader robotic system using ROS nodes and interfaces.Build benchmarks, profile and debug end-to-end inference pipelines, and validate performance in real-world robotic scenarios.Collaborate closely with AI researchers, robotics engineers, and hardware teams to translate cutting-edge research into robust, deployable edge solutions.Ensure the reliability, robustness, and stability of deployed models operating continuously in challenging, resource-constrained environments.
What You Have:
5+ years of professional experience developing and deploying deep learning models for edge, embedded, or real-time systems.BS, MS, PhD, or equivalent experience in Computer Science, Robotics, Electrical/Computer Engineering, or a related field.Strong proficiency in PyTorch, C++, Python, and CUDA for AI/ML development and model optimization.Hands-on experience with TensorRT, ONNX, and Triton, including authoring custom plugins for TensorRT.Proven experience applying model optimization techniques such as quantization, pruning, and distillation in production systems.Deep understanding of hardware constraints and performance tuning on Jetson / ARM platforms, GPUs, and embedded Linux systems.Experience integrating AI models into ROS-based robotic systems.Ability to work independently while collaborating effectively in a fast-paced, cross-functional engineering environment.
The Extras That Set You Apart:
Experience with ROS2.Experience writing and optimizing custom CUDA kernels and low-level GPU performance tuning.Familiarity with Triton, ML compilers, or compiler-level optimizations for GPU inference.Experience with JAX or additional ML frameworks beyond PyTorch.Background deploying AI systems on real robots operating in the field, not just offline or in simulation.Familiarity with NVIDIA’s edge and robotics ecosystem (e.g., Isaac ROS, DeepStream, JetPack).
Why This Role Matters
At Field AI, autonomy lives or dies on the edge. This role directly determines whether state-of-the-art AI models can run reliably and in real time on robots deployed in the field. At the Staff level, you will shape how edge AI is built, optimized, and deployed across the organization, influencing both technical direction and execution quality.
