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webAI is building distributed AI infrastructure for edge computing, enabling enterprises to deploy large-scale AI models on consumer hardware while keeping data private. The company focuses on secure, scalable AI systems for real-world deployment.
As an Edge AI Systems Engineer supporting Public Sector initiatives, you will design, build, and optimize production-ready AI systems for secure and distributed environments. This role bridges tactical hardware, machine learning, signal processing, and multisensor engineering to enable autonomous field deployment in restricted and disconnected environments.
Key responsibilities include:
- Coordinating with AI and Network Software Engineers to design low-latency multi-node communication frameworks for restricted/disconnected environments
- Quantizing, compiling, and deploying real-time multi-agent pipelines across diverse edge architectures
- Developing algorithms for asynchronous multimodal data fusion, tracking, and signal processing
- Architecting and maintaining high-fidelity Hardware-in-the-Loop simulation environments for autonomy algorithm testing
- Leading physical assembly, system calibration, and ruggedized field-testing of multi-node prototypes in unstructured environments
Required qualifications:
- 6+ years of professional experience in RF and networking architecture, tactical radio systems, physical bench-test simulation, and distributed middleware
- Hands-on experience with at least two of: embedded/edge AI systems, computer vision, signal processing, multimedia pipelines, or custom hardware integration
- Strong understanding of SWaP-C optimization, network partitioning, multi-stream ingestion, and cross-platform development (x86, Jetson/ARM64)
- Proficiency in Python, C++, and deep Linux fluency
- Bachelor's degree in relevant field or equivalent practical/military experience (advanced field experience preferred)
Preferred: Experience with autonomous software on tactical military/defense systems, field validation, site calibration, or operational testing of edge networks and ruggedized hardware in defense or commercial environments.
The ideal candidate is based in Austin, Texas and able to work primarily in-person. Exceptionally qualified candidates in Washington D.C./Northern Virginia or fully remote may be considered on a case-by-case basis, with possible travel to Austin headquarters.
