Software Engineer- Edge AI & Perception Engineering

Edge Ai & Perception Engineer Robotics

The role bridges the gap between ML research and real-world robotic systems. You'll take trained vision models and make them performant and production-ready on embedded hardware—not by doing the research yourself, but by deeply understanding the path from a PyTorch checkpoint to optimized edge inference. You'll be responsible for the full perception pipeline: ingesting sensor data, executing models in real time, and feeding actionable results to downstream robotics software.

Day-to-Day Responsibilities

• Optimize pre-trained computer vision models for NVIDIA embedded platforms, primarily using TensorRT and supporting toolchains
• Own the perception stack from end to end—depth camera ingestion, point cloud processing, and real-time object detection leveraging commercial sensor models
• Design and run experiments to evaluate perception approaches; make data-driven trade-off decisions on what belongs in production
• Wire OpenCV- and PyTorch-based inference pipelines into the team's ROS 2 architecture
• Integrate stereo camera hardware (ZED and similar) and make pragmatic use of vendor-provided SDK models where appropriate

What You Bring Core Requirements

• At least 7 years building software for robotic systems
• Deep hands-on experience with ROS 2—authoring nodes, orchestrating topic/service/action patterns, and designing clean system architectures
• A systems-level perspective: you're comfortable at the boundary between hardware drivers, real-time control loops, and application-layer software
• Strong Python skills; working proficiency in C++ (able to read, modify, and extend existing codebases confidently)
• Prior experience deploying and debugging software on NVIDIA Jetson platforms (Orin, Xavier, or comparable)
• Self-directed and effective in early-stage, greenfield development environments

Perception & Edge Deployment

• Production-level experience with PyTorch and OpenCV—not just prototyping, but building pipelines that need to run reliably
• Practical experience with model optimization workflows: TensorRT, ONNX conversion, DeepStream, or equivalent tooling
• Solid grasp of perception fundamentals including depth estimation, 3D point cloud processing, and detection architectures
• Track record of independently evaluating and benchmarking technical approaches rather than solely executing against a spec

Nice to Have

• Experience with visual SLAM, sensor fusion, or related localization techniques