Edge AI Systems Engineer, webAI

• 6+ years of professional experience in RF and networking architecture, tactical radio systems, physical bench-test simulation environments, and distributed middleware
• Hands-on experience with at least two of the following: embedded & edge AI systems, computer vision, signal processing, multimedia pipelines, custom hardware integration
• Strong understanding of physical SWaP-C optimization, network partitioning, multi-stream ingestion and cross-platform development across x86 and Jetson/ARM64
• Demonstrated expertise in Python, C++, deep fluency in Linux
• Bachelor’s degree in a relevant field, or equivalent practical experience/military service. Advanced field experience is heavily preferred

Desirable

• We are seeking an Edge AI Systems Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments
• This role sits at the intersection of real-world tactical hardware, machine learning, signals processing, and multisensor engineering, translating them into autonomous field deployment
• You will be responsible for deploying and prototyping AI models into embedded hardware systems for government edge environments in restricted and/or disconnected environments
• The ideal candidate will be based in Austin, Texas and able to work primarily in person
• However, for exceptionally qualified candidates, the Company may, in its sole discretion, consider applicants located in the Washington, DC/Northern Virginia area or, on a case-by-case basis, candidates who are fully remote
• Qualified candidates who are not based in Austin, Texas may be asked to travel to our Austin, Texas headquarters
• Coordinate with AI and Network Software Engineers to design low-latency multi-node communication frameworks in restricted and/or disconnected environments
• Quantize, compile, and deploy real-time multi-agent pipelines for readiness across diverse edge architectures
• Develop algorithms for asynchronous multimodal data fusion, tracking, and signal processing
• Architect and maintain high-fidelity Hardware-in-the-Loop simulation environments to test autonomy algorithms
• Lead physical assembly, system calibration, and ruggedized field-testing of multi-node prototypes in unstructured environments