Lead Hardware Engineer (Embedded IoT)

TL;DR

Lead Hardware Engineer (Embedded IoT): Own the full lifecycle of edge device platform from hardware selection and bring-up through deployment pipelines, remote management, and integration with retail systems with an accent on reliable operation in uncontrolled field environments. Focus on architecting systemd services, Docker orchestration for CV inference, AWS IoT Core/Greengrass integration, and optimizing AI workloads on constrained hardware like Raspberry Pi and NVIDIA Jetson.

Location: Remote

Company

AI-powered retail security and loss prevention platform with edge computer vision for micromarkets, convenience stores, and enterprise retail.

What you will do

• Design and maintain edge compute solutions on Raspberry Pi CM4/CM5, NVIDIA Jetson, and similar platforms, including hardware selection and validation.
• Architect systemd services and Docker orchestration for reliable CV inference workloads with resource efficiency.
• Own AWS IoT Core and Greengrass integrations for device provisioning, telemetry, fleet management, and OTA updates.
• Integrate IP cameras via RTSP/ONVIF and POS systems for real-time transaction correlation and video pipeline reliability.
• Optimize AI model inference through quantization, TensorRT, ONNX, and hardware profiling for memory, thermal, and power constraints.
• Document architectures, runbooks, and collaborate cross-functionally while leveraging AI coding tools.

Requirements

• 5+ years hands-on with embedded Linux and edge hardware in production.
• Deep expertise in AWS IoT Core and Greengrass for provisioning, fleet management, and OTA.
• Strong Python for production services; Linux systemd mastery.
• Experience with Yocto Project, Docker on constrained hardware, RTSP/ONVIF cameras, POS integrations.
• Hands-on with NVIDIA Jetson and Raspberry Pi CM4/CM5 in production.
• Design for failure: reconnection, degradation, observability, recovery.

Nice to have

• SOC 2 familiarity for change management and audits.
• Computer vision pipelines and ML deployment experience.
• Hardware-aware optimizations like TensorRT/ONNX/quantization.
• Retail tech background in loss prevention or CCTV.
• Custom PCB design or hardware BOM ownership.

Culture & Benefits

• Small, high-output team with real autonomy and accountability.
• High-leverage role impacting physical product reliability in real stores.
• Thrive on real-world complexity in uncontrolled environments.
• Expect active use of AI coding tools and rigorous documentation.