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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 Corporation’s All\-Domain Integration (ADI) Department (N174\) partners with the Department of the Air Force to accelerate the delivery of integrated command\-and\-control capabilities. Through expertise in mission engineering, systems integration, systems engineering, architecture, modeling and simulation, operational analysis, and artificial intelligence, MITRE provides independent technical leadership supporting advanced operational capabilities.
The ADI Department is seeking a Lead Artificial Intelligence / Machine Learning Engineer to support an Air Force program at Hanscom Air Force Base, Massachusetts. This customer\-facing role will evaluate, guide, and advance AI/ML work in the geospatial domain, with emphasis on imagery enhancement, geospatial modeling, and the application of advanced AI techniques to mission needs.
The successful candidate will provide technical leadership to government and original equipment manufacturer (OEM) stakeholders, guide architectures and implementation approaches, and help shape strategic plans and technology roadmaps for geospatial AI/ML capabilities.
Key Functions
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- Serve as a lead technical advisor for AI/ML applications in the geospatial and imagery\-analysis domain.
- Evaluate and guide AI/ML architectures, designs, implementation approaches, and system\-engineering activities supporting Air Force mission needs.
- Apply expertise in geospatial modeling, imagery enhancement, computer vision, deep learning, data augmentation, classification techniques, and search optimization.
- Develop and assess AI/ML approaches that improve the quality, usability, and exploitation of geospatial imagery and related data products.
- Provide technical leadership for the use of PyTorch, Python, Pandas, AI\-assisted code\-generation tools, and other industry\-standard AI/ML development environments.
- Collaborate directly with government customers and OEM vendors to translate mission needs into technically sound AI/ML solutions.
- Guide technical trade studies, experimentation, prototyping, and evaluation of emerging geospatial AI/ML capabilities.
- Develop strategic plans, architectures, and roadmaps that evolve the program’s geospatial AI/ML capabilities.
- Communicate technical findings, recommendations, risks, and opportunities clearly to senior government leaders, technical teams, and vendor partners.
- Support in\-person work in classified and unclassified environments, including work performed in a SCIF.
Expected Outcomes
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The successful candidate will:
- Advance the Air Force’s ability to apply AI/ML to geospatial analysis and imagery\-enhancement challenges.
- Provide trusted technical leadership for AI/ML architecture, design, implementation, and system\-engineering decisions.
- Improve the maturity, performance, and operational relevance of geospatial AI/ML capabilities.
- Enable informed customer and acquisition decisions through clear technical assessments and actionable recommendations.
- Establish a strategic AI/ML roadmap that aligns technical investments with mission priorities.
- Strengthen collaboration among government stakeholders, OEM vendors, and MITRE technical teams.
Basic Qualifications:
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- Typically requires a minimum of 10 years of related experience with Bachelor’s degree in computer science, artificial intelligence, data science, engineering, geospatial science, or a related technical field; or 8 years and a Master’s degree; or a PhD with 5 years’ experience; or equivalent combination of related education and work experience.
- Experience guiding complex technical architectures, design, implementation, systems engineering, and strategic technology roadmaps.
- Demonstrated expertise in AI/ML techniques, including deep learning, computer vision or image processing, data augmentation, classification, and search optimization.
- Experience applying AI/ML to geospatial analysis, imagery enhancement, geospatial modeling, or related mission applications.
- Demonstrated mastery of the PyTorch framework.
- Strong Python proficiency, including experience with Pandas and modern AI/ML development tools, including AI\-assisted code\-generation tools.
- Strong customer\-facing communication, leadership, collaboration, and technical\-influence skills.
- Ability to work in person at Hanscom Air Force Base, including in SCIF and non\-SCIF environments.
- Experience working directly with government customers and OEM vendors.
- Ability to travel within the continental United States approximately 10–15%.
- *Must have* *an active* *Top Secret* *U.S Government issued Security Clearance with the ability to obtain and maintain a* *Top Secret/SCI* *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 requires a minimum of 4 days a week on\-site.
Preferred Qualifications
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- Advanced degree in artificial intelligence, machine learning, computer science, engineering, geospatial science, or a related field.
- Active SCI clearance.
- Experience supporting Department of the Air Force, Department of War, intelligence community, or other national\-security geospatial missions.
- Experience with geospatial data pipelines, remote sensing, sensor data, imagery exploitation, or geospatial intelligence systems.
- Experience leading AI/ML technical assessments, prototypes, demonstrations, or transition activities from research to operational use.
- Familiarity with responsible AI, AI assurance, model evaluation, model risk management, and secure AI/ML development practices.
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/SCISalary compensation range and midpoint:
$191,200 \- $239,000 \- $286,800 AnnualWork Location Type:
Onsite
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Commitment 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
MITRE AI Hiring
MITRE has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hanscom AFB, MA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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