Senior Embedded Systems Engineer

WHAT YOU GET TO DO:

• Maintain end-to-end system integrity across the SmartGate hardware-software stack: AXIS IP cameras, NVIDIA Jetson edge compute devices, Savant video pipeline, LTE modems, routing hardware, outdoor enclosures, and power systems; test new hardware generations, firmware versions, and software releases against the production stack before fleet-wide rollout

• Build, configure, and validate complete hardware kits ahead of customer deployments; verify power budget compliance for solar-capable configurations; coordinate with installation contractors on setup expectations and post-install validation

• Deploy and update SmartGate software across 30+ edge and cloud gates using the Ansible-based Fleet Management system; manage hardware inventory and prepare Jetson and camera kits for shipment to new sites

• Remotely diagnose and resolve failures across the deployed fleet, including Savant pipeline lockups, camera connectivity issues, and networking misconfigurations; perform on-site visits when remote resolution is not achievable

• Develop and maintain commissioning runbooks and validation procedures; collaborate with the Computer Vision team on hardware-side model validation and root cause analysis for missed or incorrect gate events

• Test and benchmark which SmartGate pipeline workloads can run directly on AXIS camera hardware versus the Jetson; AXIS cameras include the ARTPEC-8 chip with an onboard AI inference engine (Larod) and support custom applications via the AXIS Camera Application Platform (ACAP).

REQUIRED QUALIFICATIONS:

• Linux: 2+ years of production experience with embedded or server-side Linux, including service configuration, log analysis, and process-level debugging; NVIDIA Jetson experience is a strong plus

• Scripting: 2+ years writing Python and/or Bash for production use, including diagnostic tools and operational automation in Linux or embedded environments

• Networking: working knowledge of IP networking (subnets, VLANs, DNS, routing, firewalls) and cellular/LTE; able to independently isolate and resolve edge-to-cloud connectivity failures

• Hardware Integration: production experience integrating edge compute platforms (NVIDIA Jetson or comparable), IP cameras, and supporting hardware into validated, production-ready systems; working knowledge of DC power constraints and solar-capable configurations

• IP Camera Systems: experience configuring, testing, and troubleshooting IP cameras in production environments; familiarity with camera-side compute capabilities and the distinction between on-camera and edge-device workloads

• Video Streaming & Encoding: experience with real-time video transport protocols such as RTSP and SRT, including configuring, and troubleshooting. Able to diagnose streaming issues and instability across edge and cloud systems. Comfortable working across the full video pipeline, from camera output through network transport to decoding and visualization.

• Video and Image Encoding Fundamentals: Understanding of modern video codecs such as H.265/HEVC and their trade-offs in compression efficiency, bandwidth usage, latency, and compute requirements. Familiar with key encoding parameters (bitrate, GOP structure, profiles, presets) and how they impact image quality, artifacting, and system performance. Strong grasp of image fundamentals including resolution, pixel density, compression formats, and file size implications, with the ability to make informed decisions balancing visual quality, storage, and transmission constraints in edge-based computer vision systems.

• Remote Troubleshooting and Fleet Management: track record of diagnosing and resolving hardware and software failures without physical device access; experience with Ansible or comparable tools for managing remote device fleets

• Able to travel occasionally within the U.S. for on-site troubleshooting when remote resolution is not possible

• Communication: ability to document technical findings clearly and coordinate with contractors, engineering teammates, and customer contacts.

PREFERRED QUALIFICATIONS:

• Familiarity with edge AI inference pipelines such as NVIDIA DeepStream, Savant, TensorRT; model training is not required

• Experience with AXIS camera systems and the AXIS Camera Application Platform (ACAP)

• Experience with Docker in embedded or edge computing environments

• Background in logistics, warehousing, or industrial technology is a plus

• Experience authoring operational documentation, kit validation procedures, or runbooks in an environment where processes are being built from scratch.