Robotics | Edge AI | Deployment & Optimization

About the Role

We’re looking for a Mid–Senior Embedded AI Engineer to help deploy and optimize AI models on a custom robot platform. You’ll work closely with the AI and embedded software teams to take trained RL policies, vision models, and LLM components and make them run reliably, efficiently, and in real time on Jetson-based onboard compute.

If you enjoy squeezing performance out of edge hardware and making advanced models work on real robots, this role is for you.

What You’ll Do

• Deploy RL policies, vision models, and LLM components onto Jetson-based edge devices

• Optimize inference performance (latency, memory, power) using TensorRT and related tooling

• Integrate AI models into ROS2-based robotic systems

• Build and maintain deployment pipelines using Docker

• Debug and optimize AI workloads running fully onboard the robot

• Collaborate closely with AI researchers and embedded software engineers to bridge research and production

What We’re Looking For

• Mid–Senior level experience in embedded or edge AI systems

• Strong C++ or Python

• Experience with ROS2

• Hands-on experience with TensorRT/ONNX and model optimization

• Experience deploying ML models on NVIDIA Jetson platforms

• Comfortable working with Docker in production systems

• Strong systems mindset and attention to performance and reliability

Nice to Have

• Experience deploying RL policies on robots

• Familiarity with vision models or multimodal systems

• Prior work on real-time or safety-critical robotic systems

Why Join

• Work on a custom, real-world robot

• See your work directly affect robot behavior in the real world

• Fast-paced Series A environment with high ownership and impact

• Fully onboard compute — no “cloud-only” robotics demos here

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