Interested in this AI/ML Engineer role at Kansas City National Security Campus?
Apply Now →Skills & Technologies
About This Role
The Kansas City National Security Campus (KCNSC), managed and operated by Honeywell Federal Manufacturing & Technologies, is a premier advanced manufacturing facility that supports the safety, security, reliability and effectiveness of our nation's nuclear deterrent. With nearly 7,000 employees combined in Kansas City, Missouri, and Albuquerque, New Mexico, KCNSC protects our nation and allies by producing trusted national security products and services for the U.S. Department of Energy's National Nuclear Security Administration.
We create innovative solutions to complex national security challenges, and lead with accountability, collaboration and an unwavering dedication to our mission and core values. Click here to view the mission, vision and values that guide our work.
Are you ready to do work that matters?
Summary
Contributes to the end-to-end design, development, productionization, and ongoing support of advanced artificial intelligence solutions for manufacturing or business applications, ensuring models are scalable, reliable, and seamlessly integrated into frontend and backend systems.
Duties and Responsibilities
- Develop AI/ML models and algorithms using disciplined software-engineering practices, including version control, automated testing, and thorough documentation.
- Contribute to designing, building, and maintaining production-grade pipelines for model training, validation, deployment, and monitoring, applying CI/CD and modern orchestration and deployment practices.
- Optimize model performance for latency, throughput, and resource utilization to meet real-time manufacturing or business requirements.
- Collaborate with multiple stakeholders to translate business needs into AI system specifications, define service-level objectives, and provide technical guidance to data science partners.
You Must Have
- Bachelors degree in quantitative field
- At least two years relevant experience in computer science, data science, or related technical activities
- Ability to travel as determined by the needs of the business
- Ability to work on-site as directed by management and as determined by the needs of the business
- Regular and reliable attendance is an essential function of this job
- United States Citizenship
- Ability to obtain and maintain, if required for position, a U.S. Department of Energy (DOE) security clearance (some positions require additional DOE designations)
We Value
- Ability to grasp complex technical and business problems, prioritize tasks, and devise innovative, production ready AI solutions that align with manufacturing or enterprise objectives.
- Demonstrated software engineering discipline: version control, unit/integration testing, continuous integration-continuous deployment (CI/CD), and documentation of code and system architecture.
- Strong communication skills (verbal, written, presentation) to convey technical concepts to stakeholders, collaborate with cross-functional teams, and produce clear technical specifications and run books.
- Deep knowledge of machine learning and deep learning concepts, including model design, training, evaluation, and optimization for performance, latency, and resource utilization.
- Familiarity with distributed data processing frameworks and big data ecosystems (e.g., Spark, Kafka) to support high throughput training and real time inference pipelines.
- Proven ability to create actionable visualizations or dashboards that surface model performance, data quality, and operational metrics for end users and decision makers.
- Experience with API development, microservices architecture, and version control systems (Git).
- Hands-on experience deploying AI applications on cloud platforms such as Azure or AWS.
- Solid understanding of Large Language Models (LLMs), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG).
This job description is not all-inclusive; additional duties may be assigned. Employees must maintain regular, reliable attendance, and be able to work on-site and travel as needed.
What We Offer
We are focused on attracting, hiring and retaining talented people who power our mission. Our robust total rewards package recognizes performance, fuels development and supports flexibility. Key features include:
- Medical, dental and vision insurance
- Health Savings Account (HSA)
- Industry leading 401(k) match
- Generous paid time off
- Flexible work schedule
- Tuition Reimbursement
- Professional Certification & License Programs
- Mission driven culture
Click here to learn more about our benefits and culture.
Connect with us on social media: Facebook, LinkedIn and X.
We are required by the Department of Energy (DOE) to conduct a pre-employment drug test and background review, including checks of references, credit, law enforcement records and employment/education history. Applicants must be able to obtain and maintain a DOE Q-level security clearance, which requires U.S. citizenship. Dual citizenship may affect clearance eligibility.
All employment offers are contingent on a federal background investigation. Substance abuse, illegal drug use, falsified information, criminal activity, serious misconduct or other indicators of untrustworthiness may result in denial or loss of clearance and termination of employment.
Honeywell Aerospace is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status and any other protected class under state or federal law.
National Nuclear Security Administration (NNSA) Requirements for MedPEDs
If you have a medical portable electronic device (MedPED) — such as a pacemaker, defibrillator, drug‑releasing pump, hearing aids or diagnostic equipment used to measure or monitor body functions — you may be required to meet NNSA security requirements if employed by us.
If you have a MedPED and are selected for an on-site interview, additional steps may be required to ensure compliance with NNSA security requirements before your interview date.If you require a reasonable accommodation to submit an application, please send an email to our Reasonable Accommodation Support Team at [email protected].
Additional Information:
- Job ID: 5613
- Category: Engineering
- Level of Experience: Experienced Professional
- Posting Location: KCNSC North 9221 Ward Parkway Kansas City, MO, 64114
- Remote Eligibility: On-Site
- Travel Required: 0-10%
- Approved Work States: MO; KS
- Hourly/Salary: Salary
- Division: 800
- Department: E65
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Kansas City National Security Campus, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Kansas City National Security Campus AI Hiring
Kansas City National Security Campus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Kansas City, MO, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.