Software Engineer- Edge AI & Perception Engineering

Edge AI & Perception EngineerRobotics • Embedded Compute • Computer VisionThe RoleThis 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 ResponsibilitiesOptimize pre-trained computer vision models for NVIDIA embedded platforms, primarily using TensorRT and supporting toolchainsOwn the perception stack from end to end-depth camera ingestion, point cloud processing, and real-time object detection leveraging commercial sensor modelsDesign and run experiments to evaluate perception approaches; make data-driven trade-off decisions on what belongs in productionWire OpenCV- and PyTorch-based inference pipelines into the team's ROS 2 architectureIntegrate stereo camera hardware (ZED and similar) and make pragmatic use of vendor-provided SDK models where appropriateWhat You BringCore Requirementst least 7 years building software for robotic systemsDeep hands-on experience with ROS 2-authoring nodes, orchestrating topic/service/action patterns, and designing clean system architecturessystems-level perspective: you're comfortable at the boundary between hardware drivers, real-time control loops, and application-layer softwareStrong 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 reliablyPractical experience with model optimization workflows: TensorRT, ONNX conversion, DeepStream, or equivalent toolingSolid grasp of perception fundamentals including depth estimation, 3D point cloud processing, and detection architecturesTrack 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