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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.
Digital Engineering \& Systems Transformation is aimed at leveraging advanced technologies and enablers to drive engineering innovation, speed and efficiency. This role is aligned to the Digital Engineering \& Systems Transformation department. MITRE is seeking a Multidiscipline Engineer with emphasis on DevOps and AI to join a dynamic and multi\-disciplined organization involved in innovating solutions for complex technical systems. MITRE seeks professionals with various expertise (e.g. software tools, processes, DevSecOps, CI/CD, MLOps, AI\-Enabled Systems, cloud services, agile methodologies, transition design, etc.) to work with a team of experienced experts working in digital transformation efforts.
Roles \& Responsibilities:
- Expanding prototyping and proof of concepts while advancing systems engineering practices to improve sponsor outcomes.
- Increasing the use of modeling throughout the SELC and utilizing modeling to advance the implementation of Systems of Systems.
- Leveraging AI and ML (including LLMs) and emerging technologies to improve systems methodology and accelerate speed of delivery for our sponsors.
- Developing reusable artifacts and prototypes.
- Improving system interoperability with a focus on System of Systems and enhancing data management practices and solutions for more efficient data management.
- Modernizing legacy IT infrastructure; implementing secure, scalable, and efficient IT solutions; accelerating adoption of cloud and emerging IT technologies; and promoting automation to enhance operational efficiency and delivery for sponsors.
Basic Qualifications:
- Bachelor’s degree in Computer Science, Computer Engineering, Systems Engineering, or a related technical discipline (e.g., Electrical Engineering, Data Science, or Applied Mathematics)
- 0–2 years of relevant experience in hands on software development, systems, and/or DevOps , including internships, co\-ops, or full\-time roles
- Practical understanding of the software development lifecycle, with experience using modern programming languages such as Python, Java, or C\+\+
- Familiarity with DevOps concepts such as automation, testing, CI/CD pipelines, or containerization (e.g., Docker, GitHub Actions, or Jenkins)
- Foundational understanding of cloud platforms (e.g., AWS, Azure, or GCP) and how they support scalable, distributed systems
- Exposure to Agile or Scrum methodologies and collaborative software development practices
- Strong analytical, communication, and teamwork skills, with demonstrated ability to learn new tools and technologies quickly
- Ability to maintain and obtain a Secret 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 50% hybrid on\-site
Preferred Qualifications:
- 1–2 years of experience or strong project background applying DevOps, MLOps, or AI/ML techniques to real\-world or simulated systems
- Experience with cloud\-native technologies or infrastructure as code (e.g., AWS, Azure, or GCP), including provisioning IaaS/SaaS and automating deployments to enhance operational efficiency
- Familiarity with large language models (LLMs) and emerging AI tools (e.g., OpenAI, Hugging Face, LangChain) and interest in applying them to engineering workflows or digital transformation efforts
- Experience in software development process, tools and languages including Java, Python, SQL, JavaScript, XML, JSON, HTML/CSS, Shell, MySQL, MongoDB and Git
- Experience developing or deploying applications using modern frameworks, APIs, or microservices architectures
- Exposure to modeling and simulation for systems design, digital twins, or model\-based systems engineering (MBSE)
- Knowledge of data management principles, database design, and approaches for working with structured and unstructured data
- Experience with prototyping and promoting reuse of artifacts and prototypes
This requisition requires the candidate to have a minimum of the following clearance(s):
NoneThis requisition requires the hired candidate to have or obtain, within one year from the date of hire, the following clearance(s):
SecretSalary compensation range and midpoint:
$86,000 \- $107,500 \- $129,000 AnnualWork Location Type:
HybridCommitment 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. Mid-level AI roles across all categories have a median of $200,000.
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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