Artificial Intelligence & Machine Learning Systems Engineer

Honeywell•Colorado Springs, CO

2d•$170,500 - $245,200

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. Major Duties & Responsibilities:

Requirements

Bachelor’s in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or related field
7 plus 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
Must be a US Citizen due to contractual requirements

Nice To Haves

Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or related field
Experience as the technical lead for establishing and accrediting classified AI/ML information systems under the DoD Risk Management Framework (RMF): Author and maintain System Security Plans (SSP), Security CONOPS, and AI/ML-specific risk annexes
Build and harden multi-enclave classified development, integration, and operational environments (RHEL 8/9, SELinux enforcing, DISA STIGs, Assured Compliance Assessment Solution (ACAS))
Lead the creation of AI/ML-specific artifacts for eMASS packages, including model cards, data provenance, adversarial robustness testing, and continuous monitoring plans
Obtain and maintain Authority to Operate (ATO) for classified cognitive EW systems containing advanced GPU/NPU-accelerated AI infrastructure
Perform Linux systems administration at the classified level: kernel tuning for real-time determinism, custom security hardening, cross-domain solution integration, auditd/ELK stack management, and FIPS 140-3 compliant cryptography
Deep Linux systems administration and hardening experience in classified environments (RHEL/CentOS, STIG compliance, SELinux policy authoring).
Hands-on experience authoring RMF packages and obtaining ATOs for systems containing machine learning components for the U.S. Government (Army, Navy, Air Force, or IC customer)
Expertise with Docker, container hardening (CIS, OSCAP), and Kubernetes in disconnected tactical environments
Experience or exposure with implementing Government reference architectures
Experience with neuromorphic or spiking neural network hardware (Intel Loihi, BrainChip Akida)
Experience with distributed training, GPU acceleration, and high-performance ML compute
Strong background in foundation algorithms, transformers, or multimodal AI
Knowledge of automated model monitoring, drift detection, and lifecycle management
Experience integrating ML models into consumer or enterprise products
Language: C/C++, GoLang, Powershell, Carbon, Java, Python, Javascript, CUDA, OpenCL, VHDL
Orchestration/deployment: Kubernetes/k3s, containerd, OpenVino, OSGi
Distributed: Hazelcast, REST architecture, websockets, NEO4J
DevSecOPS: Cmake, Maven, Ansible, Google JIB, Gradle, Jenkins, Git, Helm
Visualization: Node.js, React.js, Material UI
System administration: Linux, Windows, VMWARE
GenAI: Pytorch, Tensorflow

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.