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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.Department Summary:
Do you want to engineer software systems that contribute to solving our nation’s most critical problems? Do you want to work alongside engineers, scientists, and domain specialists that are experts in their fields? Are you passionate about best practices for delivering the highest quality software solutions?
We're making a difference every day—working for a safer, healthier, and more secure nation and world.
At MITRE, you’ll apply your skills and experience to solve tangible, high\-impact challenges facing our nation. You’ll contribute to diverse projects in support of our government sponsors while benefiting from meaningful learning and growth opportunities. Our workplace reflects our values: we invest in your development so you can build a long\-term career here. Join us for mission\-driven work, competitive benefits, exceptional professional development, and a culture of innovation that values flexibility, collaboration, and career growth.
Roles \& Responsibilities:
MITRE is seeking a Senior Agentic Software Engineer to design, build, secure, and operate mission\-focused software systems and data platforms using state\-of\-the\-art agentic and AI\-enabled software development techniques. In this role, you’ll apply modern tools and practices to develop prototypes and field\-ready capabilities, advise sponsors on effective adoption, and help establish guidance, best practices, and guardrails for responsible use. You’ll deliver features and technical solutions, shape system architecture and engineering process improvements, and lead both sustained program execution and rapid response mission needs.
The applicant should be prepared to work across multiple domain areas and to support a mix of standard program work and urgent, high\-impact enablement tasks as organizational priorities evolve. Areas of engagement may include
- Design, develop, test, and deploy mission software and data platforms using agentic/AI\-assisted workflows with clear human accountability and review
- Stand up and operate agentic development toolchains (code/test/review agents, copilots, automation bots) integrated into standard engineering practices
- Rapidly prototype mission capabilities with agentic methods and mature them into production\-grade systems (security, reliability, scalability, observability)
- Define, implement, and enforce guardrails for agentic engineering (approved tools/models, data handling, secrets, provenance, auditability, safe prompting)
- Establish evaluation and quality gates for AI\-generated artifacts (tests, static/security analysis, coding standards, CI checks, acceptance criteria)
- Architect, build, and operate distributed cloud systems (AWS/multi\-cloud) using agentic techniques to manage performance, resilience, cost, and troubleshooting
- Design and integrate APIs, backend services, and data flows; build and operate data pipelines and orchestration where applicable
- Automate DevSecOps and CI/CD with AI\-aware controls (supply\-chain security, dependency/container/IaC scanning, SAST/DAST, policy\-as\-code) and repeatable releases
- Produce and maintain documentation, runbooks, and compliance\-ready evidence for AI\-assisted engineering, including observability integrations (e.g., Splunk)
Basic Qualifications:
- Typically requires a minimum of 5 years of related experience with a Bachelor’s degree; or 3 years and a Master’s degree; or a PhD with relevant experience who can immediately contribute to this job step
- Entrepreneurial, innovative, and collaborative spirit and the curiosity to explore and push the boundaries of software development and software solutions
- Strong software engineering fundamentals and Python proficiency, including secure coding, code review, automated testing, and maintainable design patterns
- Hands\-on experience using agentic/AI\-assisted development tools (e.g., copilot, code agents, test generation, PR review automation) and integrating them into the Software Development Life Cycle
- Experience designing guardrails for AI\-enabled engineering (tool/model selection, prompt/context hygiene, secrets handling, provenance, auditability, and policy/compliance constraints)
- Experience establishing quality/evaluation practices for AI\-generated outputs (automated checks, unit/integration tests, static/security analysis, and human\-in\-the\-loop review standards)
- Leadership/enablement skills: mentoring, setting engineering standards, and advising stakeholders/customers on responsible adoption and change management
- Eligible to obtain and maintain 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:
- Advanced degree within technical discipline; Software Engineering, Computer Science, Computer Engineering, Mathematics, etc.
- Active Top\-Secret clearance preferred
- Proven experience building, deploying and operating LLM/agent systems (e.g., RAG, tool/function calling, orchestration frameworks) in production, including constrained/classified environments
- Mature evaluation and quality practices for LLMs/agents (e.g., golden sets, regression benchmarking, judge models; hallucination/jailbreak resistance and task success metrics)
- Platform engineering/DevSecOps expertise: CI/CD delivery platforms, artifact management, and software supply\-chain/provenance controls (e.g., SBOMs, signing/attestation/SLSA, policy\-as\-code)
- Cloud\-native operations depth across Kubernetes and IaC/multi\-cloud, with strong observability (tracing/logs/agent telemetry, audit logging/Splunk) and MLOps/model serving (hosting, gatewaying, rate/cost controls, monitoring)
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:
$129,200 \- $161,500 \- $193,800 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 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. Senior-level AI roles across all categories have a median of $230,000.
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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