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About This Role
Description
The Leidos Mission Solutions Business Area Office of Technology (OOT) under the INTEL SECTOR has an opening for an Artificial Intelligence (AI) Lead.
Position Summary
The Mission Solutions Business Area (BA) AI Lead will serve as the lead AI strategist and practitioner across the Business Area, responsible for defining, aligning, and accelerating Artificial Intelligence strategy and execution across the Business Areas. This leader will drive mission\-relevant, secure, and operationally deployable AI solutions that modernize intelligence tradecraft, accelerate mission software delivery, and deliver differentiated cyber and multi\-INT capabilities at speed and scale.
The MSBA AI Lead will collaborate with the BA Chief Technology Officer (CTO), BA solution architects, line managers and growth leaders to drive AI enabled solutions into program and mission operations, captures, proposals, and non\-traditional acquisitions. The MSBA AI Lead will proactively establish relationships and meet with current and potential customers to demonstrate the company's credentials for solving problems with technically differentiated AI and advanced automation solutions. The MSBA AI Lead will partner with the broader Leidos Enterprise, AI research teams, and external partners to stay informed on and evangelize Leidos and industry’s AI capabilities, tailored to Mission Solutions Business Area audiences.
As a key member of the CTO team, you will play a central role in shaping how AI is applied across mission systems—turning emerging technologies into actionable solutions that drive innovation, inform strategy, and differentiate Leidos in the market.
The position will report to the CTO for the Mission Solutions Business Area.
Primary Responsibilities
- Work with BA CTO to define and execute the Mission Solutions Business Area AI Strategy aligned to Business Area growth priorities, customer mission needs, and SECTOR and Corporate AI direction.
- Accelerate adoption of Agentic AI, Generative AI, AI\-assisted software development, AI\-enabled cyber operations, and multi\-INT fusion platforms.
- Ensure AI capabilities are mission\-aligned across Intelligence Production \& Analyst Workflows, TCPED modernization, mission software automation, cyber operations, and Information Operations platforms.
- Partner with Solution Architects, Chief Architects, and Growth leaders to shape AI\-infused solutions into captures and proposals.
- Demonstrate ready\-now AI platforms ahead of RFP release and support OTAs, CSOs, and outcome\-based acquisition models.
- Establish and maintain an AI Governance Framework ensuring secure and trusted AI deployment in classified and mission\-critical environments.
- Ensure Responsible AI practices, model evaluation standards, lifecycle risk management, and Zero Trust alignment.
- Build and lead a community of AI practitioners across the Business Area, ensuring:
- Every senior technical leader is conversant and proficient in AI\-enabled mission solutions
- Shared AI reference architectures, reusable components, and model libraries
- Reuse of IP across programs
- Transition AI prototypes (IRAD/CRAD) into operational programs at speed.
- Lead technical demonstrations and prototypes to counter disruptors and position Leidos as a mission\-ready AI integrator.
- Actively support priority captures and recompetes by:
- Writing and reviewing AI\-enabled solutions
- Coaching solution architects on the design and implementation of AI into bids and white papers
- Engaging directly with IC and DoD customers to shape AI requirements
Primary Focus Areas
- Applied AI / Machine Learning Systems and Intelligence Automation
- Intelligence and Data Analytics
- Cybersecurity and Secure AI
- Integration of AI into mission and enterprise architectures
- Systems and Enterprise Engineering
- Cloud and Digital Modernization
Basic Qualifications
- Master’s degree or Ph.D. and 15\+ years of experience in Computer Science, Artificial Intelligence, Machine Learning, Engineering, Applied Mathematics, or related field (or equivalent experience).Additional years experience may be used in lieu of a degree.
- Minimum 15\+ years of experience in AI/ML, advanced analytics, or mission software engineering.
- Demonstrated experience operationalizing AI in classified or mission\-critical environments.
- Experience with Natural Language Processing (NLP), multi\-agent systems, and sentiment analysis.
- Experience leading research and development related to the application of AI and automation into cyber operations (offensive, defensive, or information\-operations)
- Experience deploying Generative AI / LLMs, Knowledge Graphs or other technologies to improve analytic activities
- Experience leading AI solutioning in capture and proposal efforts.
- Proven experience working with distributed, highly technical teams.
- US Citizen with Active TS/SCI with CI Poly.
Preferred Qualifications
- Well\-developed strategic mindset with ability to communicate and inspire others.
- Knowledge of DoD, Federal, and Intelligence Community customers.
- 5\+ years supporting U.S. Intelligence Community or DoD/DoW customers.
- Experience modernizing TCPED workflows using AI.
- Strong executive communication skills and ability to engage senior IC leadership.
Why Leidos
At Leidos, you’ll collaborate with forward\-thinking experts across disciplines, apply emerging technologies to real mission challenges, and grow into a role that shapes the future of national security technology.
If you are a creative, systems\-level thinker who thrives at the intersection of technology, innovation, and national security—this is your opportunity to make an impact.
Join us and define how AI moves from concept to mission impact.
If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.
Original Posting:
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March 31, 2026
For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
Pay Range:
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Pay Range $154,050\.00 \- $278,475\.00
The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
About Leidos
Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16\.7 billion for the fiscal year ended January 3, 2025\. For more information, visit www.Leidos.com.
Pay and Benefits
Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at www.leidos.com/careers/pay\-benefits.
Securing Your Data
Beware of fake employment opportunities using Leidos’ name. Leidos will never ask you to provide payment\-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the Leidos.com automated system – never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment\-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at LeidosCareersFraud@leidos.com.
If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission.
Commitment to Non\-Discrimination
All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.
Salary Context
This $154K-$278K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Leidos, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($216K) sits 30% above the category median. Disclosed range: $154K to $278K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Leidos AI Hiring
Leidos has 34 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer. Positions span Houston, TX, US, Reston, VA, US, OH, US. Compensation range: $91K - $278K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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