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Honeywell · 3 days ago

Artificial Intelligence & Machine Learning Systems Engineer

Colorado Springs, CO, United States

Full-time

Onsite

Senior Level

$170K/yr - $213K/yr

7+ years exp

Honeywell is a leading software-industrial company dedicated to introducing state-of-the-art technology solutions. They are seeking a highly skilled Artificial Intelligence & Machine Learning Systems Engineer to architect, design, and develop advanced AI/ML systems for defense customers, focusing on real-time solutions and mission-critical applications.

AerospaceElectronicsInformation TechnologyInnovation ManagementManufacturingService Industry

Growth Opportunities

No H1B

U.S. Citizen Only

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

Qualification

AI/ML systems engineeringReal-time applicationsEmbedded systems programmingCI/CD pipelinesKubernetes orchestrationDockerMachine learning algorithmsData engineering workflowsLinux systems administrationTeam mentoringCollaborationProblem-solvingCommunication

Required

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

Preferred

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