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About This Role
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:
Mission Computing is a new division being established to accelerate how MITRE turns technical possibility into mission capability. We're bringing together supercomputing, AI services, secure lab and cloud infrastructure, and modern engineering tooling, all organized as a front-line function that shapes how teams across MITRE build, experiment, and deliver. The AI and Mission Enablement department is the applied AI and services adoption arm of Mission Computing. Its role is to make MITRE's AI and mission platform investments practical for mission work: giving staff usable paths to approved tools, workflows, reusable patterns, data integrations, development environments, hands-on experimentation, and high-touch technical support. The department helps teams move beyond isolated experiments into secure, repeatable ways of using platforms and computing environments in all aspects of their work.
As Mission Computing stands up, AI and Mission Enablement will sit between infrastructure and practice. The department will work with platform teams, mission users, laboratories, Enterprise Technology, Information Security Services, and AI communities across MITRE to standardize how capabilities are discovered, integrated, taught, supported, and scaled across approved data and security environments. Its work is to make AI and platforms easier to learn, easier to trust, easier to connect to MITRE workflows, and easier to apply responsibly to mission problems. AI Enablement is the group that turns platforms, APIs, agents, data workflows, secure compute, and developer experiences into a working foundation for a mission-centric MITRE.
Roles & Responsibilities:
MITRE's AI and Mission Enablement Department within the Mission Computing Division is seeking an Intermediate AI Solutions Architect to serve as a technical individual contributor within AI Enablement. The Intermediate AI Solutions Architect will apply established methods and solution patterns to develop and integrate AI-enabled applications, workflows, and data connections for mission users. The role contributes to team delivery and adoption, with guidance from more senior staff as needed. Roles and responsibilities include:
- Engage technical and mission users to clarify objectives, workflows, data needs, security constraints, and routine adoption challenges; translate defined needs into prototypes, integration tasks, and implementation guidance within established solution approaches.
- Develop, integrate, test, and document AI-enabled applications and workflows using approved tools, services, APIs, agents, retrieval approaches, data sources, and developer environments; apply existing architecture patterns and seek review when needed.
- Support secure, responsible, and repeatable use of AI in mission, engineering, analytic, and software-development workflows by applying established onboarding, evaluation, data-handling, and support procedures.
- Analyze alternative approaches to routine technical problems using technical experience, judgment, and established precedents; troubleshoot integration issues, document tradeoffs, and escalate unfamiliar or complex risks to more senior staff.
- Adapt and improve existing AI workflows, prompt patterns, agentic components, starter kits, data integrations, and development templates; contribute tested examples and technical guidance to shared enablement resources.
- Deliver routine demonstrations, training, office hours, and technical support for approved AI capabilities; explain technical information clearly, resolve common user issues, and recommend improvements based on recurring feedback.
- Collaborate with mission users, AI communities, platform teams, security stakeholders, and related engineering teams to complete defined work; expand knowledge of MITRE capabilities and processes and provide informal guidance to new or junior team members.
Basic Qualifications:
- Minimum of 2 years of related experience with a Bachelor's degree; or a Master's degree in computer science, artificial intelligence, machine learning, data science, software engineering, systems engineering, or a related technical field; or equivalent combination of related education and work experience.
- Must be eligible to obtain and maintain a Secret U.S Government issued Security Clearance within one year of hire. Per the U.S. Government's eligibility requirements, you must be a U.S Citizen to be considered for a security clearance.
- Experience developing, integrating, testing, or supporting applied AI solutions, AI/ML workflows, AI-enabled applications, software services, data integrations, developer environments, or related technical solutions.
- Working technical knowledge of applied AI, AI/ML workflows, LLM APIs, software services, data infrastructure, or developer tooling sufficient to complete a standard range of individual and team assignments using established approaches.
- Experience translating defined user or project needs into working technical components, prototypes, workflows, integrations, documentation, or user guidance; ability to analyze possible solutions to routine technical problems.
- Ability to manage assigned work, exercise judgment within established guidelines, seek guidance when needed, communicate complex technical information in routine situations, and build productive relationships with technical and nontechnical collaborators.
- Ability to work a hybrid schedule with a minimum on-site presence of 3 days per-week.
Preferred Qualifications:
- Graduate degree in computer science, artificial intelligence, machine learning, data science, software engineering, systems engineering, or a related technical field.
- Hands-on experience with LLM APIs, enterprise AI platforms, agentic tools, Model Context Protocol (MCP) services, retrieval-enabled systems, AI application development, or AI-enabled developer experiences.
- Experience prototyping, integrating, testing, or documenting AI applications, agentic workflows, retrieval solutions, reusable prompts, starter kits, or development-environment templates.
- Experience helping users adopt software or AI tools through demonstrations, training, tutorials, office hours, user support, or hands-on experimentation.
- Experience connecting AI capabilities to software delivery, DevSecOps, data platforms, analytics, knowledge management, or mission workflows; familiarity with containers, Kubernetes, MLOps, or LLMOps.
- Experience working in on-premises, hybrid, cloud, or controlled computing environments and following established security, access-control, and data-handling requirements.
- Experience with data intake, preparation, curation, retrieval, governance, or secure data workflows that support AI applications or analytics.
- Experience contributing reusable technical patterns, setup guidance, evaluation tests, support documentation, or informal knowledge transfer that helps team members and users apply approved capabilities.
- Active Secret clearance.
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:
$97,600 - $122,000 - $146,400 AnnualWork Location Type:
Hybrid 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 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 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
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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