Artificial Intelligence & Machine Learning Systems Engineer-Cognitive Electronic Warfare; EW

Delivering mission-critical, electronic solutions that protect lives. Use your creativity and critical thinking to take our products from concept to customer.

At CAES by Honeywell, we engineer solutions for the world's most critical missions. We serve customers in the defense and aerospace markets. Seeking a career that offers challenging, diverse projects and opportunities? Looking for a position with a company that offers long-term professional advancement? Searching for a place that values a diverse, team-based environment? One that values YOU. Consider CAES by Honeywell.

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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.

• 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 Dev Sec Ops  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.

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or related field
• 7+ years of professional experience shipping production AI/ML systems, ideally in defense, aerospace, or autonomous systems
• Prior work on DoD cognitive EW programs
• Deep expertise in high-performance and real-time applications (not just scripting wrappers)
• Real-time and embedded application programming (no Python-only backgrounds)
• Proven track record of deploying AI/ML solutions to cloud and edge/constrained devices
• Strong systems engineering background: you understand clocks, interrupts, DMA, cache hierarchies, memory-mapped I/O, and real-time scheduling
• Hands-on experience building and securing CI/CD pipelines for classified or regulated environments
• Expertise with Docker, container hardening, and Kubernetes in disconnected/edge configurations (k3s, microk8s, Rancher Harvester).
• Familiarity with RF/ML intersections: signal detection & classification, modulation recognition, emitter geolocation, fingerprinting, adaptive waveform design, or reinforcement learning for EW
• Proficiency with ML algorithms (including NLP, Computer Vision, time-series), libraries including foundational understanding and expertise in statistics probability theory and linear algebra
• Strong understanding of machine learning fundamentals: supervised/unsupervised learning, deep learning, model evaluation, optimization, feature engineering, etc
• Experience with data engineering workflows and building robust training datasets

• Experience as the technical lead for establishing and accrediting classified AI/ML information systems under the DoD Risk Management Framework (RMF):
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