Duties
At NSA, AI Engineering is a specialized discipline that intersects data science, software engineering, data engineering, and systems engineering, focusing specifically on the unique challenges of building, deploying, and maintaining artificial intelligence (AI) systems at scale. AI Engineers combine expertise from each of these domains to address the entire AI lifecycle, including infrastructure management, efficient model training, production deployment, performance monitoring, and continuous optimization. As an AI Engineer, you will help design and implement mission-critical AI systems that keep NSA at the cutting edge of intelligence collection, processing, and reporting.
Responsibilities include:
- 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 retuning and 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 Al solutions against mission requirements and produce actionable recommendations.
Requirements
Conditions of employment
- All applicants and employees are subject to random drug testing in accordance with Executive Order 12564.
Qualifications
ENTRY
(U) Note that different degree fields have different requirements as described below.
(U) 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.
(U) 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.
(U) 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
(U) Note that different degree fields have different requirements as described below.
(U) 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.
(U) 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.
(U) 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
(U) 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.
(U) 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.
(U) 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
(U) 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.
(U) 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.
(U) 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.
Education
The qualifications listed are the minimum acceptable to be considered for the position.
Applicants will be asked to complete the Data Science Examination (DSE) which evaluates their knowledge of statistics, mathematics, and computer science topics that pertain to data science work. Passing this examination at a local testing site is a requirement in order to be considered for selection into a data scientist position. Upon passing the examination, applicants will be evaluated for the minimum qualifications outlined in this ad. Transcripts for each academic institution are required prior to being invited to interview with Agency data science professionals and should be submitted as part of the online application. Unofficial transcripts are fine at this stage.
(U) 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.
Additional information
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.
Salary Range: $87,362 - $197,200
(Entry - Expert) Salary range varies by location, work level, and relevant experience to the position.
Training will be provided based on the selectee's needs and experience.
Benefits: NSA offers a comprehensive benefits package.
Work Schedule: This is a full-time position, Monday - Friday, with basic 8hr/day work requirement between 6:00 a.m. and 6:00 p.m. (flexible).
Candidates should be committed to improving the efficiency of the Federal government, passionate about the ideals of our American republic, and committed to upholding the rule of law and the United States Constitution.
A career with the U.S. government provides employees with a comprehensive benefits package. As a federal employee, you and your family will have access to a range of benefits that are designed to make your federal career very rewarding. Opens in a new windowLearn more about federal benefits.
Eligibility for benefits depends on the type of position you hold and whether your position is full-time, part-time or intermittent. Contact the hiring agency for more information on the specific benefits offered.
How you will be evaluated
You will be evaluated for this job based on how well you meet the qualifications above.
- 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