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About This Role
Why choose between doing meaningful work and having a fulfilling life? At MITRE, you can have both. That's because MITRE people are committed to tackling our nation's toughest challenges—and we're committed to the long\-term well\-being of our employees. MITRE is different from most technology companies. We are a not\-for\-profit corporation chartered to work for the public interest, with no commercial conflicts to influence what we do. The R\&D centers we operate for the government create lasting impact in fields as diverse as cybersecurity, healthcare, aviation, defense, and enterprise transformation. We're making a difference every day—working for a safer, healthier, and more secure nation and world. Our workplace reflects our values. We offer competitive benefits, exceptional professional development opportunities for career growth, and a culture of innovation that embraces adaptability, collaboration, technical excellence, and people in partnership. If this sounds like the choice you want to make, then choose MITRE \- and make a difference with us.
The Air \& Space Intelligence Department delivers impactful engineering analyses to rapidly transform current and future warfighter operations in contested and denied environments. We accomplish this through close interaction with our United States Air Force (USAF), United States Space Force (USSF), Office of the Under Secretary of War for Intelligence \& Security (OUSW(I\&S)), and Intelligence Community sponsors, as well as senior leadership across the Intelligence, Acquisition, and Warfighting communities.
Our engineering analyses are informed by intelligence assessments of adversarial capabilities and threats to U.S. mission systems. We conduct engineering assessments of advanced and evolving threats, perform end\-to\-end mission and effects chain analysis, develop engineering architectures for intelligence applications, identify threat mitigation opportunities, and evaluate emerging technologies to improve operational effectiveness. We leverage artificial intelligence (AI), advanced analytics, and systems engineering to accelerate mission understanding, enhance decision advantage, and enable resilient intelligence capabilities across complex national security missions.
Roles \& Responsibilities:
As a Lead AI Engineer, you will provide technical leadership in applying artificial intelligence, data engineering, and systems engineering to solve complex national security challenges. You will collaborate closely with government sponsors, mission partners, and multidisciplinary MITRE teams to identify high\-value opportunities for AI\-enabled mission transformation and guide the development of technical strategies that improve operational effectiveness.
Responsibilities include:
- Lead the application of artificial intelligence, machine learning, data engineering, systems engineering, and enterprise IT approaches to support N962 technical strategy, modernization initiatives, and mission execution.
- Partner with government sponsors to identify mission challenges and translate operational needs into AI\-enabled capabilities, engineering strategies, technical roadmaps, and implementation plans.
- Evaluate emerging AI technologies—including large language models (LLMs), natural language processing (NLP), knowledge graphs, retrieval\-augmented generation (RAG), workflow automation, and decision\-support capabilities—for applicability to Intelligence Community and Department of Defense missions.
- Identify, prioritize, prototype, and assess AI\-enabled opportunities that improve mission effectiveness through automation, advanced analytics, and operational decision support.
- Lead technical analyses supporting enterprise architecture, systems integration, infrastructure modernization, cloud adoption, data movement, and digital transformation initiatives.
- Develop engineering approaches that integrate AI capabilities into operational workflows, mission systems, and enterprise data environments while ensuring scalability, security, resilience, and mission relevance.
- Guide technical evaluations of AI solutions by assessing operational value, technical feasibility, implementation risk, data readiness, model performance, and responsible AI considerations.
- Develop technical architectures, engineering documentation, implementation roadmaps, and repeatable processes that enable successful deployment and sustainment of AI\-enabled mission capabilities.
- Collaborate across directorates, technical teams, acquisition organizations, and mission partners to align modernization efforts, technical exchanges, implementation activities, and strategic priorities.
- Prepare and deliver technical reports, executive briefings, and recommendations that communicate complex AI, data, and systems engineering concepts to both technical and executive audiences.
- Serve as a trusted technical advisor to government sponsors and MITRE leadership on emerging AI technologies, digital modernization strategies, and mission\-focused engineering solutions.
Basic Qualifications:
- Typically requires a minimum of 8 years of related experience with a Bachelor’s degree; or 6 years with a Master’s degree; or 3 years with a PhD; or an equivalent combination of education and relevant experience.
- Demonstrated experience leading technical strategy involving artificial intelligence, machine learning, systems engineering, enterprise IT, data engineering, or digital modernization initiatives.
- Experience identifying, prioritizing, and implementing AI\-enabled capabilities that improve operational effectiveness, decision support, automation, or mission execution.
- Experience translating sponsor mission needs, operational challenges, and technical requirements into actionable engineering strategies, implementation plans, or technical recommendations.
- Experience evaluating technical alternatives and balancing mission impact, implementation risk, technical feasibility, and operational constraints.
- Demonstrated ability to collaborate across multidisciplinary technical teams and effectively engage government sponsors, mission partners, and senior leadership.
- Strong written and verbal communication skills with the ability to communicate complex AI, systems engineering, and technical architecture concepts to both technical and non\-technical audiences.
- Understanding of Intelligence Community analytic processes, mission workflows, and intelligence operations.
- Familiarity with mission systems, technical architectures, operational tactics, techniques, and procedures (TTPs), or other repeatable engineering and operational processes.
- Active Top Secret/SCI security clearance.
- U.S. Citizenship required
- This position requires a minimum of 4 days a week on\-site
Preferred Qualifications:
- Experience developing, integrating, evaluating, or deploying AI\-enabled solutions using machine learning, large language models (LLMs), natural language processing (NLP), retrieval\-augmented generation (RAG), knowledge graphs, or agentic AI capabilities.
- Knowledge of current artificial intelligence, data engineering, commercial cloud, enterprise IT, and modern software engineering technologies and practices.
- Experience translating sponsor mission challenges into deployable AI\-enabled capabilities, prototypes, technical demonstrations, or operational decision\-support tools.
- Experience designing and evaluating AI\-enabled workflows that improve mission performance, automate repeatable processes, or enhance operational decision making.
- Experience assessing AI solution performance, operational suitability, technical feasibility, data readiness, implementation risk, and mission value.
- Familiarity with Responsible AI principles, model evaluation, AI assurance, governance, human\-in\-the\-loop decision support, and trustworthy AI practices within mission\-sensitive environments.
- Experience supporting Intelligence Community, Department of Defense, or other national security organizations.
- Experience developing technical roadmaps, modernization strategies, enterprise architectures, engineering assessments, or executive\-level technical briefings.
- Experience leading multidisciplinary engineering efforts, mentoring technical staff, and influencing technical direction across complex sponsor engagements.
This requisition requires the candidate to have a minimum of the following clearance(s):
Top Secret/SCIThis requisition requires the hired candidate to have or obtain, within one year from the date of hire, the following clearance(s):
Top Secret/SCISalary compensation range and midpoint:
$174,000 \- $217,500 \- $261,000 AnnualWork Location Type:
OnsiteCommitment to Non\-Discrimination
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local or international law.
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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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MITRE, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
MITRE AI Hiring
MITRE has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span McLean, VA, US, Springfield, VA, US, Huntsville, AL, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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