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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 MITRE Special Operations and Joint Staff Department (N552\) partners with the U.S. Special Operations community to solve complex national security challenges by delivering innovative, mission\-driven engineering and technical expertise. Working across government, industry, and academia, we accelerate the development, integration, and transition of advanced capabilities that strengthen operational effectiveness, reduce mission risk, and help ensure Special Operations Forces (SOF) maintain decision advantage in contested environments.
We are seeking a strategic\-minded, technically skilled, and operationally focused Artificial Intelligence Engineer to serve in a full\-time sponsor environment supporting U.S. Special Operations Forces (SOF). The successful candidate will bring deep experience developing, deploying, and transitioning AI/ML\-enabled capabilities in support of complex national security missions. Working directly with DoD and Intelligence Community sponsors, operators, and mission partners, this individual will develop, integrate, and operationalize advanced data science, artificial intelligence, and machine learning capabilities that improve mission planning, operational effectiveness, and decision\-making.
N552 operates as a globally integrated team supporting USSOCOM Headquarters, Joint Special Operations Command (JSOC), Service Component Special Operations Commands (SOCs), and Theater Special Operations Commands (TSOCs). Engineers work directly with operators, acquisition organizations, and mission partners to identify emerging challenges, evaluate advanced technologies, and transition mission\-ready capabilities into operational use.
This position offers the opportunity to work alongside Special Operations Forces and government sponsors, applying advanced data science and artificial intelligence to some of the nation's most challenging national security problems while rapidly delivering innovative capabilities to operational users.
Roles and Responsibilities:
Qualified candidates will have a strong background in data science, machine learning, and software engineering, with demonstrated success applying advanced analytics to complex operational problems. This role includes designing and implementing technical solutions, conducting applied research and rapid prototyping, and collaborating directly with government stakeholders to improve mission outcomes.
The selected candidate will work closely with Special Operations Department leadership and technical teams across MITRE to develop tactical, operational, and strategic capabilities supporting the Department of Defense and Intelligence Community. Success in this role requires working effectively within MITRE's federated environment by partnering across NSEC, MITRE Labs, and other organizations while engaging directly with SOF operators and government technical experts.
Candidates should possess hands\-on experience developing production\-quality software, machine learning applications, and data analytics solutions, with the ability to rapidly prototype, deploy, and transition capabilities across multiple mission areas.
Selected candidates will demonstrate the following:
- Domain/Technical Competence. Demonstrated experience in two or more of the following areas: (1\) Data Science, (2\) Machine Learning, (3\) Artificial Intelligence, (4\) Computer Vision, and (5\) DoD Special Operations. Demonstrated ability to design, develop, deploy, and transition technical solutions that address operational needs.
- Leadership and Execution. Build trusted relationships with legal, policy, intelligence, technical, and operational stakeholders. Coordinate technical efforts across multiple projects while working directly with SOF program teams to deliver solutions supporting specialized missions and sensitive activities.
- Communication and Teamwork. Lead and mentor technical teams to achieve mission objectives while clearly communicating technical approaches, risks, and impacts to sponsors and stakeholders. Collaborate with engineers, planners, analysts, and mission specialists to deliver actionable solutions that address operational needs.
- Sponsor Engagement. Build enduring relationships with senior military leaders, engineers, and technical experts to understand mission needs, shape technical direction, and deliver operationally relevant solutions.
- Flexibility and Suitability. Demonstrate the agility to adapt as sponsor priorities and operational requirements evolve. Leverage MITRE's technical reach\-back capabilities to rapidly assemble the right expertise at the right time while operating effectively within SOF culture and sensitive mission environments.
Basic Qualifications:
- Typically requires a minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years’ experience or equivalent combination of related education and work experience.
- Bachelor's degree in Data Science, Artificial Intelligence, Computer Engineering, Computer Science, Cybersecurity, or a related technical field.
- Hands\-on experience designing, building, deploying, and transitioning prototype systems for DoD or Intelligence Community sponsors.
- Experience developing AI/ML solutions, including traditional machine learning, deep learning, and large language model (LLM)\-enabled applications using frameworks such as PyTorch, TensorFlow, Codex/Claude CLI, LangChain, or equivalent.
- Experience developing scalable data processing and analytics solutions using distributed computing frameworks such as Spark, Hadoop Distributed File System, Delta Tables, or similar technologies.
- Strong software development skills in Python/PySpark/Scala, with experience developing production\-quality software using modern engineering practices, including Git, automated testing, documentation, and CI/CD workflows.
- Experience with Linux operating systems and terminal use.
- Experience developing data science and machine learning applications using open\-source tools such as NumPy, Pandas, SciPy, Scikit\-Learn, SQL, and related frameworks.
- Experience integrating, processing, and analyzing structured, unstructured, geospatial, temporal, and multi\-source operational data.
- Demonstrated success leading capability development efforts and transitioning operational prototypes to government sponsors.
- Active Top Secret clearance with the ability to obtain SCI and a polygraph, as required.
- Per the U.S. Government’s eligibility requirements for a clearance, U.S Citizenship is required.
- This position requires a minimum of 4 days a week on site.
Preferred Qualifications:
- Master's degree in Data Science, Artificial Intelligence, Computer Engineering, Computer Science, Cybersecurity, or a related technical field.
- Experience leading technical projects for DoD or Intelligence Community sponsors, including managing multidisciplinary teams and executing work within secure operational environments.
- Experience applying advanced technical solutions to support mission needs in highly constrained, operational, or denied environments.
- Demonstrated success identifying operational capability gaps and developing innovative technical solutions for DoD or Intelligence Community sponsors.
- Experience with big data architectures, distributed analytics, and cloud\-based data processing technologies (e.g., Spark, Hadoop, Python).
- Experience developing software for embedded, distributed, high\-performance, or cloud computing environments.
- Experience deploying AI/ML or data analytics capabilities into operational environments supporting military or intelligence missions.
This requisition requires the candidate to have a minimum of the following clearance(s):
Top SecretThis requisition requires the hired candidate to have or obtain, within one year from the date of hire, the following clearance(s):
Top Secret/SCI/PolygraphSalary 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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