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Location Lexington Park, Maryland Category Engineering, Technology, & Science Job Type Full time Job Id R2130592 Posted Date 09/28/2026
JOB DESCRIPTION Title:
Sr Systems Engineer (AI/ML Integration) Program Summary: This position supports tasking at the Naval Air Systems Command (NAVAIR) Program Executive Office for Unmanned Aviation and Strike Weapons (PEO(U&W)) located in Patuxent River, Maryland. The selected candidate will support the Smart Sensor team in the development, integration, testing, acquisition, and sustainment of advanced unmanned aviation capabilities. This role focuses on the application of Artificial Intelligence (AI) and Machine Learning (ML) technologies to support mission effectiveness, operational decision-making, and autonomous system capabilities across the MQ-9 program portfolio.
Job Summary: KBR is seeking a Senior Systems Engineer (AI/ML Integration) to provide technical leadership supporting AI/ML-enabled capabilities within the MQ-9 Program Office. The selected candidate will serve as a systems engineering subject matter expert responsible for guiding architecture development, requirements definition, technical evaluations, and integration activities associated with AI/ML technologies. This role requires an understanding of AI/ML concepts and their application within complex aerospace and defense systems, with a focus on evaluating technical approaches and supporting informed program decisions. The successful candidate will collaborate with government, industry, and engineering stakeholders to ensure AI/ML capabilities are effectively integrated into operational systems while supporting acquisition, test, production, and sustainment objectives.
Role and Responsibilities:
- Develop and apply systems engineering products and analyses to support the requirements, acquisition, deployment, and sustainment of unmanned aviation systems and associated autonomous capabilities.
- Support the development of AI/ML implementation strategies, concept plans, and technical roadmaps aligned with operational and program objectives.
- Define and maintain system requirements, interface definitions, and architecture products for AI/ML-enabled systems and capabilities.
- Assess, evaluate, and recommend AI/ML approaches, technologies, and architectures to support mission and operational requirements.
- Conduct technical trade studies, analyses of alternatives, and engineering assessments to inform program decisions and investment priorities.
- Coordinate with government stakeholders, development teams, test organizations, and platform integrators to support successful system implementation and fielding.
- Provide systems engineering oversight of AI/ML data management, model development, validation, integration, and deployment activities performed by technical teams.
- Support engineering investigations and deficiency analyses to identify root causes, evaluate corrective actions, and recommend technical solutions.
- Prepare and deliver technical reports, engineering assessments, briefing materials, and decision-support documentation for program leadership.
- Lead and coordinate multifunctional engineering efforts supporting AI/ML capability integration across the MQ-9 enterprise.
- Other duties as assigned in support of mission and business requirements.
Basic Qualifications:
- Education: Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, Systems Engineering, or related STEM field.
- Experience: Minimum eight (8) years of experience supporting the design, integration, test, acquisition, or sustainment of complex aerospace, aviation, autonomous, or defense systems. Minimum three (3) years of experience developing, integrating, testing, or deploying AI/ML solutions. Experience conducting requirements analysis, system architecture development, trade studies, and technical evaluations. Experience supporting systems engineering activities within a U.S. Government defense acquisition environment.
- Security Clearance: Active Secret security clearance with ability to obtain and maintain a Top Secret clearance. U.S. Citizenship required.
Preferred Qualifications:
- Master's degree in Systems Engineering, Electrical Engineering, Computer Engineering, Computer Science, or a related technical discipline.
- Experience supporting AI/ML-enabled systems, autonomous systems, sensor fusion, decision-support systems, or advanced analytics capabilities.
- Understanding of machine learning concepts, model development lifecycles, data management practices, and AI system integration considerations.
- Experience evaluating AI/ML architectures and technical approaches for operational defense applications.
- Previous experience supporting NAVAIR programs or military aviation systems.
- Experience supporting unmanned aviation platforms, particularly MQ-9 or similar Group 4/5 UAS programs. INCOSE ASEP, CSEP, or ESEP certification, or DAWIA certification.
- Experience preparing engineering reports, technical correspondence, and executive-level briefing materials using Microsoft Office applications.
Scheduled Weekly Hours: 40 hours/week.
Compensation: $105,000-$157,000. The salary range posted is based on the national average. The offered rate will be based on the selected candidate’s location, knowledge, skills, abilities, and/or experience, contract affordability, and in consideration of internal parity.
Additional Compensation: KBR may offer bonuses, commissions, or other forms of compensation to certain job titles or levels per internal policy or contractual designation. Additional compensation may be in the form of a sign-on bonus, relocation benefits, short-term incentives, long-term incentives, or discretionary payments for exceptional performance.
KBR Benefits: KBR offers a selection of competitive lifestyle benefits which could include 401K plan with company match, medical, dental, vision, life insurance, AD&D, flexible spending account, disability, paid time off, or flexible work schedule. We support career advancement through professional training and development.
Belong, Connect and Grow at KBR
At KBR, we are passionate about our people and our Zero Harm culture. These inform all that we do and are at the heart of our commitment to, and ongoing journey toward being a People First company. That commitment is central to our team of team’s philosophy and fosters an environment where everyone can Belong, Connect and Grow. We Deliver – Together.
KBR is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity or expression, age, national origin, veteran status, genetic information, union status and/or beliefs, or any other characteristic protected by federal, state, or local law.
Salary Context
This $105K-$157K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At KBR, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($131K) sits 38% below the category median. Disclosed range: $105K to $157K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
KBR AI Hiring
KBR has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lexington Park, MD, US. Compensation range: $157K - $157K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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