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About This Role
Location Chantilly, Virginia Category Engineering, Technology, \& Science Job Type Full time Job Id R2127250 Posted Date 08/03/2026
JOB DESCRIPTION
Title:
AI Systems Engineer
KBR is seeking a visionary AI Systems Engineer to join our team, providing critical technical services to the U.S. Government (USG). This role supports a dedicated USG Program Office, in shaping, designing, and deploying cutting\-edge AI and Machine Learning (AI/ML) initiatives for national security space architectures.
Position Overview
As an AI Systems Engineer, you will serve as both a hands\-on technical architect and a strategic advisor. You will leverage the Government customer’s massive enterprise datasets, spanning complex program budgets, scheduling, and financials, to build proof\-of\-concept models (including Large Language Models and financial predictive tools). You will lead the evolution of the AI roadmap by identifying cutting\-edge commercial technologies and directing successful prototypes into robust, production\-ready enterprise tools.
Responsibilities
- Strategic LLM Initiatives: Help shape and define the AI/ML roadmap for the Government customer office. Guide the government on how to securely build, adopt, govern, and maintain foundation models and custom LLMs across sensitive environments, ensuring robust model lifecycle management.
- Rapid Prototyping: Design and build hands\-on, high\-impact proof\-of\-concept models. This includes training, fine\-tuning, and leveraging Large Language Models (LLMs) for search, summarization, and business intelligence, as well as developing advanced financial, budget, and scheduling analysis models.
- Data Exploitation: Work directly with massive, unstructured and structured enterprise datasets (financials, acquisition timelines, schedules, program execution metrics). Clean, curate, and pipeline this data to create high\-quality training corpora, instruction\-tuning datasets, and knowledge bases for Retrieval\-Augmented Generation (RAG) to discover trends, anomalies, and optimization opportunities
- Technology Curation \& Scouting: Actively scout the commercial tech sector, academia, and open\-source communities for breakthroughs in generative AI, NLP, and predictive analytics. Curate a pipeline of high\-performing external groups and technologies for USG consideration.
- Contractor \& Vendor Management: Formulate technical recommendations, draft statements of work, and establish the system integration requirements needed to bring external contractors on board to scale and operationalize your prototypes.
- Technical SETA Advisory: Provide objective technical evaluation of contractor proposals, designs, and deliverables to ensure they align with government AI safety, security, and performance standards. This includes overseeing LLM red\-teaming, bias testing, hallucination mitigation, and ongoing alignment evaluations.
Work Environment
- Location: On\-Site (Chantilly, VA area)
- Travel Requirements: Minimal (0\-20%)
- Working Hours: Standard
Required Qualifications
- Clearance: Active TS/SCI with Polygraph.
- Education \& Experience:
+ Bachelor’s degree with 5\+ years of relevant experience,
+ Master’s degree with 3\+ years of relevant experience, or
+ PhD with 0\+ years of relevant experience.
- Technical Skills: Strong foundations in AI/ML concepts, natural language processing (NLP), and statistical modeling.
- Prototyping Experience: Demonstrated experience building prototypes using Python, R, or similar tools, specifically with frameworks related to LLMs (e.g., llama.cpp, LangChain, Hugging Face) or data analytics.
- Analytical Skills: Experience working with complex enterprise datasets (e.g., financial, resource planning, schedule networks, or programmatic data).
- Communication: Exceptional ability to translate highly technical AI concepts—such as LLM training methodologies, RAG architecture, model alignment, and corpus curation—into strategic, actionable business recommendations for non\-technical executives and government leaders. This includes clearly articulating the compute costs, security risks, data provenance, and ongoing maintenance requirements involved in building versus adopting language models.
Desired Qualifications
- Financial \& Schedule Modeling: Experience applying ML or advanced statistical modeling to predictive financial analysis, budget execution, risk modeling, or project scheduling.
- Enterprise AI Deployment: Understanding of the challenges of deploying AI/ML models in high\-security, air\-gapped government cloud environments (e.g., AWS C2S).
- Technology Scouting: Prior experience in technology scouting, venture capital technical vetting, or acting as an industry\-to\-government technology transition agent.
- Acquisition Support: Familiarity with the DoD/IC acquisition lifecycle, systems engineering processes, and SETA advisory roles.
Basic Compensation:
$152,600 \- $190,700
This range is for the Virginia area only
The offered rate will be based on the selected candidate’s work location, knowledge, skills, abilities and/or experience, clearance level, contract affordability and in consideration of internal parity.
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 $152K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($171K) sits 20% below the category median. Disclosed range: $152K to $190K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
KBR AI Hiring
KBR has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Huntsville, AL, US, Chantilly, VA, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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