What are the responsibilities and job description for the Artificial Intelligence & Machine Learning Systems Engineer position at Honeywell?
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.
Major Duties & 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.
Why This Role is Different:
- You will own the entire stack from algorithm research to bare-metal deployment on platforms that fly, float, or roll into harm’s way
- No Python notebooks in production, everything is compiled, containerized, signed, and deployed with cryptographic integrity
- Real impact: your code will out-think and out-maneuver adversary emitters in real conflicts. If you live for the intersection of cutting-edge machine learning and extreme systems engineering under the harshest constraints, we want to talk to you