Interested in this AI/ML Engineer role at KBR?
Apply Now →Skills & Technologies
About This Role
Title:
Senior Gen AI Developer*Belong. Connect. Grow. with KBR!*
KBR’s National Security Solutions team provides high\-end engineering and advanced technology solutions to our customers in the intelligence and national security communities. In this position, your work will have a profound impact on the country’s most critical role – protecting our national security.
KBR is seeing a Senior Gen AI Developer to join our team in El Segundo, CA (remote).
The MILSATCOM Systems Engineering, Integration, and Test (MSEIT) effort provides leading edge Systems Engineering \& Integration (SE\&I) for the US Space Force’s Space Systems Center (SSC). We support the Space Force’s acquisition of state\-of\-the\-art satellite communications systems, providing global secure, survivable, and protected communications for our nation’s warfighters. We seek technical individuals who will thrive in a highly collaborative work environment of small teams, using the most modern tools and methodologies to tackle the challenges of integrating complex space and ground communications systems.
Why Join Us?
- Innovative Projects: KBR’s work is at the forefront of engineering, logistics, operations, science, program management, mission IT and cybersecurity solutions.
- Collaborative Environment: Be part of a dynamic team that thrives on collaboration and innovation, fostering a supportive and intellectually stimulating workplace.
- Impactful Work: Your contributions will be pivotal in designing and optimizing defense systems that ensure national security and shape the future of space defense.
Work Environment:
- Location: El Segundo, CA (Work\-from\-home flexibility with on\-site support as required)
- Travel Requirements: Minimal
- Working Hours: Standard
Qualifications:
Required:
- Ability to obtain and maintain a Secret security clearance
- Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical discipline
- 15\+ years of software development experience, including 3\+ years building generative AI or LLM\-powered applications
- Strong proficiency in Python for AI/ML development, data processing, and automation
- Hands\-on experience designing, developing, and deploying applications using large language models (LLMs)
- Proven experience building and optimizing Retrieval\-Augmented Generation (RAG) pipelines using embeddings, vector databases, and semantic search
- Experience with prompt engineering, LLM orchestration, and developing agent\-based workflows
- Familiarity with LLM frameworks such as LangChain, LlamaIndex, or Semantic Kernel
- Experience integrating AI solutions with enterprise data sources, APIs, and knowledge graphs
- Ability to evaluate and fine\-tune model outputs for quality, bias, explainability, and performance
- Experience deploying and maintaining applications in cloud\-native environments (Azure, Kubernetes, CI/CD pipelines)
- Solid understanding of software development lifecycle (SDLC) and DevOps practices for production AI systems
Desired:
- Master’s in Computer Science, Artificial Intelligence, or a related field
- Experience integrating knowledge graphs and graph databases (e.g., Neo4j, ArangoDB) into LLM\-driven applications
- Exposure to multi\-modal AI models (text, image, audio) and their application in real\-world systems
- Experience building custom LLM evaluation pipelines and benchmarking frameworks
- Familiarity with model optimization techniques or edge deployment for constrained environments
- Experience deploying AI solutions in defense, government, or other high\-security environments
- Knowledge of responsible AI practices, including bias mitigation, explainability, and governance
Basic Compensation: $182,000 \- $228,000 in California
The offered rate will be based on the selected candidate’s knowledge, skills, abilities and/or experience and in consideration of internal parity.
Ready to Make a Difference? If you’re excited about making a significant impact in the field of space defense and working on projects that matter, we encourage you to apply and join our team.
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 $182K-$228K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($205K) sits 13% above the category median. Disclosed range: $182K to $228K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
KBR AI Hiring
KBR has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span El Segundo, CA, US, Chevy Chase, MD, US. Compensation range: $187K - $228K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.