Embedded AI Engineer

The role

This person will lead the development and deployment of deep learning models on IoT and edge computing platforms. They will work on converting and optimizing computer vision models for execution on high-efficiency hardware accelerators.

What you will do

• 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 on resource-constrained devices using AI accelerators.
• 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.
• 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.

Required qualifications

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, AI, or a related technical field.

Nice to have

• 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.

About the company

BusPatrol transforms how communities protect students and enforce traffic safety through intelligent, real-time video analytics and automated enforcement.