Your mission
Build AI that runs in the real world. On real robots. Under real constraints.
At Autonomous Teaming, we build autonomous robotic systems operating in extreme, GPS-denied environments. Our models run fully on edge hardware (Jetson, FPGA, custom boards), with no cloud, no fallback, no excuses.
We’re looking for an engineer who loves hard problems : real-time inference, low-latency pipelines, CUDA kernels, TensorRT graphs, and deploying ML models directly on hardware.
If you enjoy debugging things that only break on the robot, this role is for you.
Missions :
Own the full pipeline from model to real-time inference on embedded devices:
Optimize deep neural networks for Jetson, FPGA or ARM boards
Apply quantization, pruning, distillation to hit strict FPS, power and memory budgets
Convert & compile models using TensorRT, ONNX, CUDA, C++
Build ROS nodes integrating optimized perception into the full robotic system
Debug runtime failures, memory leaks, thermal throttling, kernel-level issues
Benchmark and validate performance directly on hardware
Ship models that run reliably in real-world, harsh environments
Why us?
- You ship code that directly controls real robots
- You work on constrained hardware, where every millisecond and every watt matters
- You solve problems that cloud ML engineers never face
- You own your optimizations end-to-end : from model to field deployment
- You work in a small, high-performing team where ownership is real
If you want a job with clean layers and abstract diagrams, this is not it.