AI Engineer

As an AI Engineer at NSA, you will design and implement mission-critical AI systems that keep the agency at the cutting edge of intelligence collection, processing and reporting to keep the nation safe. You’ll apply your expertise in data science, plus software, data and systems engineering to build, deploy and maintain AI systems at scale while addressing the entire AI life cycle, including infrastructure management, efficient model training, production deployment, performance monitoring and continuous optimization.

Depending on your experience level, you will be assigned to a mission office or enrolled in the three-year Data Science Development Program, which will both broaden and specialize your AI engineering skills through courses and working in a variety of mission offices (each for several months). In either case, you will work with NSA experts in AI engineering, related technical domains and specialized subject areas. You’ll have opportunities to participate in internal technical roundtables and attend technical conferences with experts from industry and academia.

AI Engineers will:

• Lead or contribute to cross-functional teams to develop and operationalize AI solutions that help solve our most challenging problems.
• Apply modern engineering techniques to design, develop, deploy and maintain end-to-end AI workflows spanning model training, inference and performance monitoring.
• Adapt and integrate diverse AI model architectures, including computer vision systems, natural language processors, audio processors, large language models (LLMs) and multi-modal frameworks to address complex mission-critical challenges.
• Monitor and maintain AI products through systematic identification of performance degradation and computational inefficiency and address these challenges through regular fine-tuning to ensure continued alignment with evolving mission needs and organizational goals.
• Maintain knowledge of current AI research and adapt emerging techniques to intelligence applications.
• Test and evaluate AI solutions against mission requirements and produce actionable recommendations.

The qualifications listed are the minimum acceptable to be considered for the position.

For all of the Engineering degrees, if program is not ABET accredited, it must include specified coursework.* *Specified coursework includes courses in differential and integral calculus and 5 of the following 18 areas: (a) statics or dynamics, (b) strength of materials/stress-strain relationships, (c) fluid mechanics, hydraulics, (d) thermodynamics, (e) electromagnetic fields, (f) nature and properties of materials/relating particle and aggregate structure to properties, (g) solid state electronics, (h) microprocessor applications, (i), computer systems, (j) signal processing, (k) digital design, (l) systems and control theory, (m) circuits or generalized circuits, (n) communication systems, (o) power systems, (p) computer networks, (q) software development, (r) Any other comparable area of fundamental engineering science or physics, such as optics, heat transfer, or soil mechanics.

For degrees in Computer Science or Engineering, entry is with an Associate’s degree plus 2 years of relevant experience, or a Bachelor’s degree and no experience, or a Master’s degree and no experience.

For degrees in Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Artificial Intelligence, Data Science, or Physical or Biological Sciences, entry is with an Associate’s degree plus 3 years of relevant experience, or a Bachelor’s degree and 1 year of relevant experience.

Relevant experience must be in one or more of the following: implementing production scale AI/ML (Artificial Intelligence / Machine Learning) solutions, distributed model training, distributed AI/ML systems, AI/ML performance monitoring, platform engineering, cloud engineering, developing deep learning models, neural networks, sustaining/maintaining AI/ML models, implementing AI/ML algorithms, AI/ML model development and deployment, DevOps, MLOps, cloud infrastructure management, software engineering, automated testing, or containerization.

FULL PERFORMANCE Note that different degree fields have different requirements as described below.

For degrees in Computer Science or Engineering, entry is with an Associate’s degree plus 5 years of relevant experience, or a Bachelor’s degree plus 3 years of relevant experience, or a Master’s degree plus 1 year of relevant experience, or a Doctoral degree and no experience.

For degrees in Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Artificial Intelligence, Data Science, or Physical or Biological Sciences, entry is with an Associate’s degree plus 5 years of relevant experience, or a Bachelor’s degree plus 3 years of relevant experience, or a Master’s degree plus 1 year of relevant experience, or a Doctoral degree and 1 year of relevant experience.

Relevant experience must be in one or more of the following: implementing production scale AI/ML solutions, distributed model training, distributed AI/ML systems, AI/ML performance monitoring, platform engineering, cloud engineering, developing deep learning models, sustaining/maintaining AI/ML models, implementing AI/ML algorithms, AI/ML model development and deployment, DevOps, MLOps, cloud infrastructure management, software engineering, automated testing, or containerization.

SENIOR Entry is with an Associate’s degree plus 8 years of relevant experience, or a Bachelor’s degree plus 6 years of relevant experience, or a Master’s degree plus 4 years of relevant experience, or a Doctoral degree plus 2 years of relevant experience.

Degree must be in Computer Science, Engineering, Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Artificial Intelligence, Data Science, or Physical or Biological Sciences.

Relevant experience must be in two or more of the following: implementing production scale AI/ML solutions, distributed model training, distributed AI/ML systems, AI/ML performance monitoring, platform engineering, cloud engineering, developing deep learning models, sustaining/maintaining AI/ML models, implementing AI/ML algorithms, AI/ML model development and deployment, DevOps, MLOps, cloud infrastructure management, software engineering, automated testing, or containerization.

EXPERT Entry is with an Associate’s degree plus 11 years of relevant experience, or a Bachelor’s degree plus 9 years of relevant experience, or a Master’s degree plus 7 years of relevant experience, or a Doctoral degree plus 5 years of relevant experience.

Degree must be in Computer Science, Engineering, Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Artificial Intelligence, Data Science, or Physical or Biological Sciences.

Relevant experience must be in three or more of the following: implementing production scale AI/ML solutions, distributed model training, distributed AI/ML systems, AI/ML performance monitoring, platform engineering, cloud engineering, developing deep learning models, sustaining/maintaining AI/ML models, implementing AI/ML algorithms, AI/ML model development and deployment, DevOps, MLOps, cloud infrastructure management, software engineering, automated testing, or containerization. Additionally, you must have experience in serving as an AI Project Team Leader/model owner.

Competencies Specialized skills and experience in one or more of the following is desired:

• Deep learning frameworks (PyTorch, TensorFlow, JAX)
• Model training, fine-tuning and optimization techniques
• Computer vision, NLP, speech/audio processing and/or multi-modal AI systems
• Large language models (LLMs) and transformer architectures
• Model evaluation, validation and performance monitoring
• Transfer learning and domain adaptation
• Python programming and other relevant languages (C++, Java, Scala, TypeScript)
• Version control (Git) and collaborative development
• API design and microservices architecture
• Software testing frameworks and CI/CD pipelines
• Containerization (Docker, Kubernetes)
• Data processing frameworks (Spark, Dask, Ray)
• Feature engineering and data preprocessing
• Production model deployment and serving infrastructure
• Monitoring, logging and observability tools
• Cloud platforms (AWS, Azure, GCP) and/or HPC systems
• Distributed computing and parallel processing
• GPU optimization and resource management
• Database systems (SQL and NoSQL)
• Cross-function collaboration and communication
• Technical documentation and presentation
• Ability to translate mission requirements into technical solutions

Pay, Benefits, & Work Schedule Pay: Salary offers are based on candidates’ education level and years of experience relevant to the position and also take into account information provided by the hiring manager/organization regarding the work level for the position.