Interested in this AI/ML Engineer role at MITRE?
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At MITRE, your passion fuels work that impacts our nation and improves lives. This is where your skills strengthen national security, cybersecurity, health, transportation, and citizen services. We’re a nonprofit engineering, applied research, and advanced technology organization working in the public interest. With objectivity and integrity at our core, we lead federally funded research and development centers for U.S. government agencies – collaborating with industry and academia to deliver integrated, high-impact solutions that advance our nation’s health, security, and prosperity. Our people tackle the toughest technical challenges, supported by competitive benefits, meaningful career development, and a culture of innovation, collaboration, and technical excellence. Choose MITRE for a career with purpose — and help shape the future.Department Summary:
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.
MITRE’s Multi-INT AI Engineering Department brings together the deep technical knowledge needed to develop new capabilities and the critical domain knowledge needed to shape them for multiple sponsor missions. That combination gives us the depth and breadth to thrive in mission-focused research and development and science and technology organizations. It also reflects the credibility we have earned with sponsors through work spanning research, analysis, prototyping, test, and evaluation. We draw on all these strengths to turn promising ideas into capabilities that serve real mission needs.
We seek motivated people with experience in artificial intelligence (AI), software development, and cloud engineering experience who want to apply their skills to meaningful mission challenges. Our teams bring together sensors, AI and machine learning (ML), and data engineering to exploit multimodal data. This includes remote sensing data collected using infrared imagery (OPIR), hyperspectral imagery (HSI), radar (SAR/GMTI), full motion video (FMV), Wide Are Motion Imagery (WAMI), and 3D data from a range of sources.
Roles & Responsibilities:
- Set technical direction for related prototype efforts, resolving architecture and integration tradeoffs and identifying research approaches suited to emerging mission needs.
- Establish data engineering approaches for complex multimodal pipelines and integrate findings across mission data sources.
- Set evaluation and study approaches across related sponsor questions, assessing the strength and limitations of the evidence.
- Lead teams or projects with notable risk, complexity, and resource requirements across related efforts.
- Align teams around shared objectives, resolve dependencies, and guide execution across technical disciplines.
- Build productive sponsor relationships, advise on technical options, and influence decisions among technical and mission stakeholders.
- Mentor technical staff and transfer expertise across the job family.
- Identify consequential mission challenges, secure support for long-term outcomes, and advise on technical investments and transition paths.
Basic Qualifications:
- Typically requires at least 10 years of related experience with a bachelor’s degree; 8 years with a master’s degree; a PhD with 5 years’ experience; or an equivalent combination of related education and work experience.
- Expert software and cloud engineering knowledge, including experience setting architecture and resolving integration and deployment tradeoffs across related AI efforts.
- Experience establishing data engineering approaches for multimodal pipelines with complex quality, integration, performance, and access needs.
- Experience setting AI evaluation strategies and applying knowledge of multi-INT workflows and mission constraints to complex studies.
- Ability to assess emerging technologies, identify significant capability gaps, and solve technical problems with lasting impact.
- Experience leading related teams or projects with notable risk, complexity, and resource requirements.
- Ability to build sponsor relationships, align stakeholders around technical decisions, mentor staff, and share expertise across the job family.
- Ability to work in classified and unclassified environments, including a SCIF.
- Must have an active Top Secret/SCI with CI Poly U.S Government issued Security Clearance. Per the U.S. Government’s eligibility requirements, you must be a U.S Citizen to be considered for a security clearance.
- This position has an on-site requirement of 5 days a week on-site.
Preferred Qualifications:
- Advanced degree in computer science, engineering, or related disciplines.
- Experience shaping multi-INT fusion approaches across related efforts and assessing how sensor characteristics affect findings.
- Experience advising on the capabilities, limitations, and mission value of large language models or agentic AI across multiple use cases.
- Experience coordinating technical studies that inform government acquisition or investment decisions.
- Track record of leading multidisciplinary efforts from prototype evaluation through recommendations for sponsor transition or investment.
This requisition requires the candidate to have a minimum of the following clearance(s):
Top Secret/SCI/PolygraphThis 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:
$191,200 - $239,000 - $286,800 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
MITRE AI Hiring
MITRE has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chantilly, VA, McLean, VA, US, Bedford, MA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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