Interested in this AI/ML Engineer role at National Security Agency?
Apply Now →Skills & Technologies
About This Role
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.
Job Summary
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.
Qualifications
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. ENTRY 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 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. Salary Range: $87,362 - $197,200 (Entry/Developmental, Full Performance, Senior, 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). DCIPS Trial Period: If selected for this position, you will be required to serve a two-year DCIPS trial period, unless you are a veterans' preference-eligible employee, in which case you are required to serve a one-year trial period. This trial period runs concurrently with your commitment to the position, if applicable. Before finalizing your appointment at the conclusion of your trial period, NSA will determine whether your continued employment advances the public interest. This decision will be based on factors such as your performance and conduct; the Agency's needs and interests; whether your continued employment would advance the Agency's organizational goals; and whether your continued employment would advance the efficiency of the Federal service. Upon completion of your trial period, your employment will be terminated unless you receive certification, in writing, that your continued employment advances the public interest. If you do not receive certification for continued employment, you should receive written notice prior to the end of your trial period that your employment will be terminated and the effective date of such termination.
How to apply
Apply soonest, as job postings can close earlier than stated end dates due to changes in requirements. It is important to review and note the minimum qualifications, as only those applicants who meet the required qualifications will be contacted to continue the employment process. Please populate the resume tool to showcase any relevant work experience and education related to the position and answer any applicable screening questions. Information collected will be used to determine eligibility, and failure to provide accurate information may result in disqualification for this position. A confirmation email will be sent after submission of the first application and also after any future updates to submitted applications. Due to time sensitive communications regarding applications, please ensure your spam filters are configured to accept email from [email protected]. For job vacancies that include stated testing requirements, also include the following: @uwe.nsa.gov, @nsa.gov, and @pearson.com U.S. Citizenship is required for all applicants. NSA is an equal opportunity employer and abides by applicable employment laws and regulations. All applicants and employees are subject to random drug testing in accordance with Executive Order 12564. Employment is contingent upon successful completion of a security background investigation and polygraph. Reasonable accommodations may be provided to applicants with disabilities during the application and hiring process where appropriate. The following four narrative questions provide an opportunity for you to highlight your dedication to public service for the hiring manager and agency leadership (or designee(s)). While your responses are not required and will not be scored, we encourage you to thoughtfully address each question. Please provide a response of 200 words or less to each question. You will be asked to certify that you are using your own words and did not use a consultant or artificial intelligence (AI) such as a large language model (LLM) like ChatGPT or Copilot. (Please attach in application) 1. How has your commitment to the Constitution and the founding principles of the United States inspired you to pursue this role within the Federal government? Provide a concrete example from professional, academic, or personal experience. 2. In this role, how would you use your skills and experience to improve government efficiency and effectiveness? Provide specific examples where you improved processes, reduced costs, or improved outcomes. 3. How would you help advance the President's Executive Orders and policy priorities in this role? Identify one or two relevant Executive Orders or policy initiatives that are significant to you, and explain how you would help implement them if hired. 4. How has a strong work ethic contributed to your professional, academic or personal achievements? Provide one or two specific examples, and explain how those qualities would enable you to serve effectively in this position.
DCIPS Disclaimer
VETERANS AND TRANSITIONING SERVICE MEMBERS Thank you for your service! The National Security Agency (NSA) is part of the Defense Civilian Intelligence Personnel System (DCIPS). All positions at NSA are in the Excepted Service under Title 10, United States Codes (U.S.C.), Section 1601 appointment authority. Veterans' Preference In accordance with the procedures provided in DoD Instruction 1400.25, Volume 2005, "DoD Civilian Personnel Management System: Defense Civilian Intelligence Personnel System Employment and Placement," NSA applies veterans' preference, as defined by Section 2108 of Title 5, U.S.C., to eligible candidates. If you are claiming veterans' preference, you are required to provide acceptable documentation of your preference eligibility upon application. Acceptable documentation includes: - DD-214: "Certificate of Release or Discharge from Active Duty," which shows dates of service and discharge under honorable conditions (Copy 4); OR - Certification of Service: A written document on letterhead from the appropriate branch of the armed forces that certifies the service member is expected to be discharged or released from active duty service in the armed forces under honorable conditions not later than 120 days after the date the certification is signed. The certification should include the military service dates, including the expected discharge or release date; AND - Standard Form 15 (SF-15) Application for 10-point Veteran Preference (http://www.opm.gov/forms/pdf_fill/sf15.pdf). If you are claiming a service-connected disability of 30 percent or more, the documentation you provide must specifically demonstrate this level of disability; AND - VA Letter of Disability (for 10pt and Sole Survivorship rating) You may obtain a letter from the Department of Veterans Affairs reflecting your level of disability for preference eligibility by visiting a VA Regional Office, contacting a VA call center, or online (https://www.ebenefits.va.gov/). **Failure to provide sufficient documentation of veterans' preference eligibility may preclude NSA from identifying you as a preference eligible candidate during the hiring selection process.
Salary Context
This $87K-$197K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At National Security Agency, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $87K to $197K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
National Security Agency AI Hiring
National Security Agency has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Fort Meade, MD, Fort Meade, MD, US. Compensation range: $197K - $197K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.