Applied Robotics Engineer General Intuition is the frontier lab for acting in space and time. We build large action models and world models that can perceive, predict, and act across virtual and physical environments. General Intuition builds on the strength of Medal, the world's largest and fastest-growing platform for gaming clips, where millions of gamers capture, share, and discover new games every year. We are looking for an Applied Robotics Engineer to bridge our research with the realities of deploying AI systems in the physical world. Our models are trained on real human decisions in simulated environments, and your job is to make them work on hardware in the real world, under constraints such as limited compute, power, noisy sensors, and unpredictable environments. You will take our models and focus on post-training, evaluation, system integration, and deployment. You will work directly with partners to understand their legacy control systems, sensor pipelines, latency constraints, and edge compute limits. This position requires both office and field work, with time working closely with customers in various locations. You'll also close the loop back to research. When a policy fails on hardware you'll find out why, whether it's sensor calibration, actuator dynamics the simulator didn't capture, control-loop timing, coordinate conventions, or a model leaning on one modality and ignoring another. This is an in-person role based in our Virginia based office.
What We're Looking For:
5+ years of experience deploying AI or robotics systems from prototype to production, spanning simulation, model development, and real-world deployment.
You have taken a model from a training checkpoint to running on real hardware and owned it end to end, including the failures.
You have debugged a model that was quietly wrong rather than obviously broken, found the root cause, and fixed the system rather than the symptom.
Strong Python and PyTorch, and you drop into C++ when performance requires it. You've done inference optimization on constrained hardware: quantization, distillation, TensorRT or ONNX export, control frequency.
Depth in at least one of the following and interest in the others: policy learning and control (RL, imitation learning); world models and simulation (Unity, Unreal, Isaac); perception and inverse dynamics models (sensor fusion, learning from demonstration).Experience across the hardware-software boundary: sensors, robotics middleware, edge compute, real-time constraints, and unreliable networks.
You're rigorous about evaluation, and you know what a benchmark number does and doesn't tell you.
Comfortable with a partner's engineers and their leadership in the same room, explaining what a model will and won't do without overselling it.
