Artificial Intelligence & Machine Learning Systems Engineer

Honeywell•Colorado Springs, CO

About The Position

We’re seeking a highly skilled Artificial Intelligence & Machine Learning Systems Engineer to architect, design, and develop advanced AI/ML systems that power our next generation of products. In this leadership role, you’ll contribute to the technical roadmap, mentor engineering teams, and collaborate with cross-functional teams to deliver intelligent, scalable, and production-ready AI and machine learning technologies. You will be responsible for researching, creating, adapting and evaluating AI/ML techniques to solve complex customer problems with real-time solutions to support our defense customers. Specifically, we are building next-generation cognitive electronic warfare systems that operate autonomously at the tactical edge in contested, low-SWaP (Size, Weight, and Power), denied, and disconnected environments. This is not a prompt-engineering or GenAI role. We are looking for hardcore AI/ML systems engineers who treat machine learning as a component of a larger, mission-critical, real-time embedded system.

Responsibilities

Design, implement, and harden on-line and continual-learning ML algorithms for RF signal classification, adaptive jamming, cognitive radar, and electronic attack/support decision engines.
Port, optimize, and deploy ML inference algorithms to edge processors.
Build and maintain low-latency, deterministic inference pipelines that integrate tightly with real-time RF front-ends and digital signal processing chains.
Lead the systems integration of AI/ML techniques into mission-critical embedded platforms running real-time operating systems.
Design and deliver warfighter-focused engineering visualizations and tactical displays (real-time spectrum awareness, threat emitter tracks, cognitive EW decision overlays, confidence heatmaps) using modern web stack frameworks that run natively on embedded tactical processors and dismounted soldier systems.
Own the MLOps and DevSecOps pipeline for classified EW programs: secure CI/CD, model versioning, containerized build/test/deploy, SBOM generation, and compliance with DoD zero-trust and CNCF security standards.
Architect and deploy Kubernetes-based edge orchestration clusters (e.g. k3s) that operate in fully air-gapped tactical environments with strict latency and availability requirements.
Perform end-to-end performance profiling (memory bandwidth, cache coherency, DMA, GPU/TPU/NPU utilization).
Review code, guide architecture decisions, and mentor the AI/ML engineering team.
Collaborate with product and engineering teams to identify AI/ML-driven opportunities.