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Embedded AI Engineer

BusPatrol
Remote, IND
Full-time
Mid
Remote
Discovered 5 days ago
Python

Full Job Posting

Key Responsibilities

  • Develop and optimize embedded software and AI model inference pipelines for real-time object detection, tracking, and classification at the edge.
  • Deploy and maintain vision-based deep learning models (YOLO, CNN, RNN, OCR) on resource-constrained devices using AI accelerators (e.g., Hailo, Jetson, Coral, ARM).
  • Lead cloud-to-edge transformation of AI models, including quantization, pruning, conversion, and hardware-aware optimization.
  • Collaborate with AI/ML researchers to integrate trained models into embedded applications and validate edge inference accuracy.
  • Optimize for performance, latency, and power efficiency in real-world mobile or vehicle-mounted environments.
  • Work with hardware abstraction layers, toolchains, and SDKs for AI acceleration (e.g., HailoRT, TensorRT, OpenVINO, ONNX, TFLite).
  • Design and implement robust, fault-tolerant inference pipelines using tools like GStreamer, OpenCV, or FFmpeg.
  • Contribute to system architecture discussions focused on scalable AI at the edge, real-time event capture, and safety-critical decision support.

Preferred Qualifications

  • Proficient in C/C++ and Python for embedded development and AI integration.
  • Experience with quantization, pruning, and model conversion for edge inference (ONNX, TFLite, TensorRT, Dataflow Compiler etc.).
  • Hands-on experience with at least one AI hardware platform (e.g., Hailo, NVIDIA Jetson, Intel Movidius, ARM Ethos-U, Google Coral).
  • Solid understanding of deep learning architectures and model behavior (CNN, YOLOv5/v8, OCR, ReID, RNNs).
  • Strong knowledge of video/image processing pipelines using GStreamer, OpenCV, or similar.
  • Experience working with Linux-based embedded systems, build systems (Yocto, CMake), and cross-compilation toolchains.
  • Understanding of IoT/vehicle edge environments, including system resource constraints, fault tolerance, and field deployment challenges.
  • Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, AI, or a related technical field.
  • Familiarity with multi-camera systems, object re-identification, or behavior/event detection at the edge.
  • Knowledge of real-time messaging and telemetry systems (MQTT, ZeroMQ, or gRPC).
  • Experience in public safety, ADAS, surveillance, or other vision-based safety-critical applications.
  • Familiarity with data privacy, security, and compliance concerns for edge AI systems.
  • Why Join Us
  • Be a core driver in bringing safety AI to the edge, where milliseconds matter.
  • Help scale an intelligent, distributed AI platform that makes real-world impact across thousands of vehicles.
  • Work at the intersection of AI, embedded systems, and public safety in a fast-paced, purpose-driven environment.
  • Collaborate with engineers, data scientists, product managers, and field ops to build AI that directly saves lives.

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