Applied Robotics Engineer

Medal; Washington (District of Columbia); On-site; USD 140,000 - 210,000; Full time

Job summary

General Intuition is hiring an Applied Robotics Engineer to bridge research with real-world hardware deployment. You’ll work on post-training evaluation, system integration, and deployment of AI models on physical systems under power, compute, and sensor constraints.

You will collaborate with partners to understand legacy pipelines, latency, and edge compute limits, ensuring robust performance in varied environments. This is an in-person role based in our Virginia office.

Qualifications

• 5+ years deploying AI or robotics systems to production.
• Experience moving a model from training to real hardware end-to-end.
• Debugged a model to find root causes and fixed the system, not just the symptom.
• Strong Python and PyTorch; C++ for performance when required.
• Experience across sensors, edge compute, real-time constraints, and unreliable networks.
• Collaborates effectively with partner engineers and leadership to explain model capabilities.

Responsibilities

• Bridge research with hardware deployment and real-world constraints.
• Lead post-training evaluation, system integration, and deployment efforts.
• Work with partners to understand legacy control systems, sensor pipelines, and edge compute limits.
• Optimize models for constrained hardware (quantization, TensorRT, ONNX).
• Investigate failures and identify root causes across the hardware-software boundary.

Skills

Python

PyTorch

C++

RL & control

Sensor fusion

Edge compute

Tools

TensorRT

ONNX

Unity

Unreal

Isaac SDK

Job description

About The Company

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've raised over $650M from Khosla, GC, Valor, and Point72 since October 2025, and recently closed our latest round at a $6.2B valuation.

The Role

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.