Artificial Intelligence & Machine Learning Systems Engineer

Position Overview

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.

Key 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, including 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.

Required Qualifications

• Bachelor’s in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a 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, including understanding of 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 (e.g. k3s, microk8s, Rancher Harvester).
• Familiarity with RF/ML intersections such as 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), and a foundational understanding of statistics, probability theory, and linear algebra.
• Strong understanding of machine learning fundamentals: supervised/unsupervised learning, deep learning, model evaluation, optimization, and feature engineering.
• Experience with data engineering workflows and building robust training datasets.

Preferred Qualifications

• Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field.
• Experience as the technical lead for establishing and accrediting classified AI/ML information systems under the DoD Risk Management Framework (RMF), including authoring and maintaining System Security Plans (SSP), Security CONOPS, and AI/ML-specific risk annexes.
• Experience in building and hardening multi-enclave classified development, integration, and operational environments.
• Proven ability to lead the creation of AI/ML-specific artifacts for eMASS packages, including model cards, data provenance, adversarial robustness testing, and continuous monitoring plans.
• Experience obtaining and maintaining Authority to Operate (ATO) for classified cognitive EW systems with advanced GPU/NPU-accelerated AI infrastructure.
• Strong Linux systems administration experience at the classified level, including kernel tuning for real-time determinism and custom security hardening.
• Hands-on experience authoring RMF packages and obtaining ATOs for systems containing machine learning components for U.S. Government customers.
• Expertise with Docker, container hardening (CIS, OSCAP), and Kubernetes in disconnected tactical environments.
• Experience or exposure to implementing Government reference architectures.
• Familiarity with neuromorphic or spiking neural network hardware (e.g. 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.

Benefits & Perks

• Compensation: The annual base salary range is $170,500 – $213,200 in select regions and $196,160 – $245,200 in others. Actual offer will consider responsibilities, experience, education, and key skills.
• Benefits: Comprehensive benefits package including employer-subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; as well as Parental Leave, Paid Time Off, and Paid Holidays. This role may also be eligible for a 9/80 schedule.
• Application: The application period is approximately 40 days from the job posting date (January 6, 2026), subject to change based on business needs and candidate availability.