Senior Principal Data Scientist / AI-ML SME (Analytic Superiority)
At a glance
Senior AI Research role at Leidos. Fort Meade · full-time · $154,050–$278,475 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Design and run AI workloads across cloud platforms, physical servers and isolated mission environments
- Tune language models and inference systems to meet throughput needs on constrained hardware
- Build retrieval systems and connect AI agents with cyber defense platforms and live network data
- Create fast cyber tools and GPU components using Python, Go, Rust or C/C++
- Apply AI to defensive threat detection and offensive vulnerability discovery and targeting
What you bring
- At least fifteen years in hands-on software engineering, data science or distributed systems
- Five or more years focused on machine learning engineering, deep learning or language-model optimization
- Three to five years in the DoD or intelligence cyber environment, building vulnerability or targeting tools
- An active TS/SCI clearance with polygraph, plus a master’s or doctoral degree in a related quantitative field
Who this fits
You’ll suit a deeply hands-on senior technical role that also calls for mentoring experienced technologists. The work is on-site at Fort Meade in a SCIF environment. Additional experience may substitute for the stated degree requirement.
From the employer
Mission Overview
The Leidos Intel Sector is looking for a premier AI/ML Subject Matter Expert (SME) to serve as a Technical Closer for our COSS 3.0 program supporting USCYBERCOM and the Cyber National Mission Force (CNMF) at Fort Meade, MD. In this elite role, you will architect and engineer the cross-platform AI frameworks required to achieve absolute analytic superiority.
You will compress both defensive cyber operations (identifying network vulnerabilities and gaps) and offensive operations (vulnerability discovery and automated targeting) from days down to minutes. As a Technical Closer, you will mentor senior technologists by example—working "fingers-on-keyboard" to solve the command's most complex technical roadblocks and pushing production-grade AI directly into multi-cloud, hybrid, and air-gapped mission enclaves.
Core Technical Requirements
Platform-Agnostic Infrastructure & MLOps: Architect, deploy, and scale distributed AI workloads across any environment required, including AWS SageMaker, Google Vertex AI, Azure Government, and bare-metal, air-gapped server racks.
Agentic AI & Cyber Automation: Deploy and optimize tools like LangGraph, CrewAI, or AutoGPT to automate cyber threat identification and offensive target generation at wire speed.
Low-Level Model Engineering & Optimization: Fine-tune open-source large language models (e.g., Llama 3, Mistral) inside secure enclaves using PyTorch or TensorFlow. Utilize NVIDIA TensorRT, Triton Inference Server, vLLM, and quantization libraries (bitsandbytes) to compress models for high-throughput execution under strict hardware constraints.
Advanced RAG Architectures: Direct the engineering of enterprise Retrieval-Augmented Generation (RAG) stacks using LangChain paired with high-performance vector databases like Milvus, Qdrant, or Pinecone.
Autonomous Cyber Integration: Connect intelligent agents directly into security orchestration platforms (e.g., Palo Alto Cortex XSIAM/XSOAR) to trigger automated network defense actions and ingest massive, real-time PCAP and telemetry streams via Apache Kafka/Spark.
Polyglot Engineering: Demonstrate engineering mastery in Python, Go, Rust, and C/C++ to build ultra-fast cyber tools, write optimized GPU kernels, and interface with distributed frameworks like Ray.
Mission & Domain Expertise
Dual-Spectrum Operations: Proven capability to support both Defensive Cyber Operations (DCO) (log parsing, behavioral threat hunting, anomaly detection) and Offensive Cyber Operations (OCO) (automated vulnerability discovery, exploit generation, payload optimization).
Mission Platform Orchestration: Experience integrating custom AI/ML pipelines into unified mission systems and high-value data streams found across Project Maven, Palantir Foundry, and tactical command frameworks.
Required Experience & Background
Total Technical Experience: 15+ years of hands-on experience in software engineering, data science, or distributed systems.
Core AI/ML Focus: 5+ years of specialized experience in Machine Learning Engineering, deep learning, or LLM optimization.
DoD/IC Ecosystem: 3–5 years working within the DoD/IC cyber ecosystem, specifically building tools that map vulnerabilities or accelerate targeting cycles.
Clearance: Active TS/SCI with Polygraph..
Work Location: On-site at Fort Meade, MD (SCIF environment).
Preferred Certifications & Military Equivalency
Industry Certifications: Google Cloud Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, or NVIDIA Generative AI/LLM Associate.
Cyber Mission Force (CMF) Equivalency: Prior certification as a CMF Exploitation Analyst (EA), Digital Network Analyst (DNA), or specialized technical experience as an Army 17A/170A, Navy 181X, or Air Force 17D/17S.
Educational Background
Primary Requirement: Master’s Degree or PhD in Data Science, Artificial Intelligence, Computer Science, Mathematics, or a related quantitative field. Additional years of experience may be considered in lieu of degree.
If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.
Original Posting:
July 16, 2026For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
Pay Range:
Pay Range $154,050.00 - $278,475.00The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.