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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, L080, 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 a Lead AI Solutions Architect to serve as a lead-level technical individual contributor within AI Enablement. The Lead AI Solutions Architect will provide technical direction for complex, cross-functional efforts that turn approved AI platforms, APIs, agents, data workflows, secure computing environments, and developer experiences into practical and repeatable mission solutions. Roles and responsibilities include:
- Lead discovery and solution-architecture engagements for complex, ambiguous, or cross-cutting mission needs; establish objectives, scope, success measures, decision criteria, and an effective path from experimentation through implementation and adoption.
- Develop and lead solution strategies and executable architectures that integrate AI applications, agentic workflows, retrieval-enabled systems, approved data sources, secure computing environments, developer tooling, platform services, and mission workflows.
- Direct technical evaluations and trade studies, synthesize multiple sources of information, identify risks and dependencies, and recommend innovative but practical approaches for secure, supported, responsible, and repeatable AI use.
- Lead functional teams, technical workstreams, or complex projects with moderate resource requirements, risks, dependencies, or organizational complexity; establish technical strategies and objectives and guide multidisciplinary contributors in their execution without formal supervisory authority.
- Establish and promote reusable AI workflows, prompt patterns, agentic components, starter kits, development-environment templates, data-integration approaches, technical standards, quality practices, and support models that improve adoption and scale impact across programs.
- Mature AI enablement practices, including capability discovery, intake, experimentation, onboarding, training, office hours, technical support, feedback loops, and transition from prototypes to supported use; identify recurring needs and opportunities to improve or scale services.
- Advise mission users, project leaders, platform teams, security stakeholders, and senior technical leaders on AI solution alternatives, constraints, sequencing, resource needs, and long-term implications; provide architecture review, mentoring, knowledge transfer, and cross-MITRE collaboration.
Basic Qualifications:
- 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.
- Extensive experience in applied AI, AI/ML workflows, AI-enabled applications, software engineering, data platforms, developer services, platform engineering, research computing, mission computing, or related technical domains, with working knowledge of related disciplines.
- Experience leading functional teams, technical workstreams, or complex projects with moderate resource requirements, risks, dependencies, or organizational complexity; developing technical strategies and objectives and guiding execution without formal people-management authority.
- Experience translating ambiguous user, project, or mission needs into executable AI solution architectures, reusable technical services, workflows, patterns, prototypes, data integrations, enablement models, implementation plans, or adoption roadmaps.
- Ability to work independently with guidance only in the most complex situations, lead others in solving complex technical problems, communicate complex concepts clearly, build consensus, and influence technical users, project leaders, platform teams, security or compliance stakeholders, and senior leadership.
- Must be eligible to obtain and maintain a Secret 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.
- Ability to work a minimum 3 days per-week on-site presence in McLean, VA or Bedford, MA and travel up to 5%.
Preferred Qualifications:
- Graduate degree in computer science, artificial intelligence, machine learning, data science, software engineering, systems engineering, human-centered computing, or a related technical field.
- Experience leading complex, customer-facing AI enablement, AI solution architecture, capability-adoption, technical-consulting, or platform-integration efforts involving multiple organizations, technical disciplines, or stakeholder communities.
- Extensive experience with LLM APIs, enterprise AI platforms, agentic tools, MCP services, Agent Skills, MLOps, LLMOps, Kubernetes, Kubeflow, GPU environments, retrieval-enabled systems, AI application development, or AI-enabled developer experiences.
- Experience leading the design, integration, and operationalization of AI capabilities across software delivery, DevSecOps, data platforms, analytics, modeling and simulation, systems engineering, knowledge management, or mission workflow platforms.
- Experience leading AI workloads or solution architectures across on-prem, hybrid, cloud, CUI-capable, classified-adjacent, or other controlled computing environments.
- Experience establishing secure and repeatable approaches for data intake, curation, exchange, governance, retrieval, AI data pipelines, or other mission AI data workflows.
- Experience establishing or improving AI training, coaching, internal enablement programs, communities of practice, office hours, rapid prototyping events, VibeLab-style challenges, hands-on experimentation, or related adoption mechanisms.
- Experience establishing reusable AI patterns, technical standards, architecture-review practices, starter kits, development templates, enablement playbooks, quality practices, or support models and transferring those practices to other technical staff.
- Active Secret or higher Security 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:
$174,000 - $217,500 - $261,000 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.
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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 11,520 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
MITRE AI Hiring
MITRE has 13 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span McLean, VA, Bridgeport, WV, US, McLean, VA, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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