Senior Principal Data Scientist / AI-ML SME (Analytic Superiorit

Leidos, Inc.
Fort Meade, MD, United States
about 2 months ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Compensation
$154,050.0 - $278,475.0
Working hours
Regular working hours

Tech stack

Artificial Intelligence Amazon Web Services ARM Architecture Microsoft Azure C++ (Programming Language) Digital Electronics Distributed Systems Python (Programming Language) Pcap Log Analysis Machine Learning Apache Maven
+18 more
Open Source Technology Tensorflow Software Engineering Data Streaming Google Cloud Pytorch Large Language Models Apache Spark Deep Learning Multi-Cloud Generative AI Information Technology Bare Metal Cortex XSOAR Platform Apache Kafka Machine Learning Operations TensorRT Cyber Warfare

Job description

The Leidos Intel Sector is looking for a premier AI/ML Subject Matter Expert (SME) to serve as a 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.

  • : 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.
  • : Deploy and optimize tools like LangGraph , CrewAI, or AutoGPT to automate cyber threat identification and offensive target generation at wire speed.
  • : 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.
  • : Direct the engineering of enterprise Retrieval-Augmented Generation (RAG) stacks using LangChain paired with high-performance vector databases like Milvus, Qdrant, or Pinecone.
  • : 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.
  • : 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.

  • : Proven capability to support both (log parsing, behavioral threat hunting, anomaly detection) and (automated vulnerability discovery, exploit generation, payload optimization).
  • : 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.

Requirements

  • : 15+ years of hands-on experience in software engineering, data science, or distributed systems.
  • : 5+ years of specialized experience in Machine Learning Engineering, deep learning, or LLM optimization.
  • : 3-5 years working within the DoD/IC cyber ecosystem, specifically building tools that map vulnerabilities or accelerate targeting cycles.
  • : Active .
  • : On-site at Fort Meade, MD (SCIF environment).

  • : Google Cloud Professional Machine Learning Engineer , AWS Certified Machine Learning - Specialty, or NVIDIA Generative AI/LLM Associate.
  • : 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.

  • : 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.

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