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WebAI

Edge AI Systems Engineer

Washington D.C. Area (Remote)fulltimemidAdded today

About this role

webAI is seeking an Edge AI Systems Engineer to deploy and optimize AI models on embedded hardware for secure, disconnected government environments. You'll work across hardware, machine learning, and signal processing to build production-ready autonomous systems for tactical field deployment.

What you'll do

  • Coordinate with software engineers to design low-latency multi-node communication frameworks for restricted/disconnected environments
  • Quantize, compile, and deploy real-time multi-agent AI pipelines across diverse edge architectures
  • Develop algorithms for asynchronous multimodal data fusion, tracking, and signal processing
  • Architect and maintain Hardware-in-the-Loop simulation environments for autonomy algorithm testing
  • Lead physical assembly, system calibration, and ruggedized field-testing of multi-node prototypes

What they're looking for

  • RF and networking architecture
  • Embedded and edge AI systems
  • Computer vision or signal processing
  • Python and C++
  • Linux
  • Hardware integration and SWaP-C optimization
  • x86 and Jetson/ARM64 cross-platform development
  • Tactical radio systems
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WebAI

WebAI builds AI infrastructure and platform solutions that help enterprises deploy and optimize machine learning systems at scale. The company is hiring Design Engineers, Software Engineers, Data Platform Engineers, Forward Deployed Engineers, and AI Software Engineers to develop everything from web experiences and AI prototypes to data connectors and distributed infrastructure.

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Likely interview questions

  • Walk us through a specific project where you deployed an AI model to embedded hardware in a resource-constrained environment—what were the key optimization challenges?
  • How do you approach model quantization and compilation for diverse edge architectures, and what trade-offs have you navigated?
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