Artificial Intelligence & Machine Learning Systems Engineer

Job Description

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

Qualifications

Required Qualifications:

• Bachelor’s in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or related field
• 7 plus years of professional experience developing 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
• Hands-on experience building and securing CI/CD pipelines for classified or regulated environments
• Expertise with containers, container hardening, and container orchestration like k3s in disconnected/edge configurations.
• 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
• Security: U.S. Citizenship Required – Active Secret, must be eligible for TS/SCI/SAP

Preferred Qualifications

• 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 Framew