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About This Role
AI / Machine Learning Engineer
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Mid\-Level / Senior Level \| $90,000 \- $110,000 \| Onsite \| Top Secret Clearance Required \| U.S. Citizenship Required
Support the modernization of strategic defense mission systems by developing advanced Artificial Intelligence (AI) and Machine Learning (ML) capabilities for U.S. Strategic Command (USSTRATCOM). Join a multidisciplinary engineering team responsible for designing intelligent software solutions, developing machine learning models, and integrating AI\-driven capabilities into mission planning and command and control (C2\) systems. You'll also help advance AI adoption across the engineering lifecycle by enabling automation, improving developer productivity, and supporting next\-generation software development practices.
A Day in the Life \- What you'll do
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- Design, develop, and deploy Artificial Intelligence (AI) and Machine Learning (ML) solutions supporting strategic mission planning and command and control (C2\) systems.
- Develop machine learning models, predictive algorithms, and data analytics solutions that improve mission effectiveness and operational decision\-making.
- Analyze structured and unstructured data to identify trends, generate insights, and support data\-driven engineering decisions.
- Develop software applications and automation tools using Python and modern AI/ML frameworks.
- Integrate AI and ML capabilities into existing operational mission systems while ensuring performance, security, and reliability requirements are met.
- Collaborate closely with systems engineers, software developers, architects, product owners, Scrum Masters, and government stakeholders throughout the software development lifecycle.
- Evaluate, test, validate, and optimize AI/ML models to improve model accuracy, scalability, and operational performance.
- Support the implementation of MLOps practices for model deployment, versioning, monitoring, and lifecycle management.
- Research emerging AI technologies, machine learning techniques, and generative AI capabilities to identify opportunities for mission modernization.
- Serve as an AI/ML technical resource by identifying opportunities to automate engineering workflows, software development, testing, and documentation throughout the engineering lifecycle.
- Develop technical documentation, model documentation, and engineering artifacts supporting AI/ML implementation and sustainment.
- Present AI/ML concepts, technical findings, and implementation strategies to engineering teams, program leadership, and government customers.
Required Qualifications
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- Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Data Science, Artificial Intelligence, or a related STEM (Science, Technology, Engineering, or Mathematics) discipline from an accredited university.
- U.S. Citizenship required.
- Active DoD Top Secret security clearance or the ability to obtain and maintain a Top Secret clearance with required special access.
- Demonstrated experience developing algorithms and solving complex analytical problems using Python.
- Experience designing, developing, or integrating Artificial Intelligence and Machine Learning solutions.
- Familiarity integrating AI/ML capabilities into operational software or mission systems.
- Strong understanding of machine learning concepts, data analytics, and statistical modeling techniques.
- Strong analytical, problem\-solving, and software development skills.
- Ability to collaborate effectively across multidisciplinary engineering teams.
- Strong written and verbal communication skills.
Preferred Qualifications
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- Experience supporting U.S. Strategic Command (USSTRATCOM) missions including Intelligence, Mission Planning, or Command \& Control (C2\).
- Experience implementing MLOps pipelines and machine learning lifecycle management.
- Experience applying AI and machine learning throughout the software or systems engineering lifecycle.
- Experience using Generative AI tools to improve software development, engineering productivity, or code generation.
- Experience evaluating, validating, and testing AI/ML models in operational environments.
- Experience supporting Air Force Nuclear Weapons Center (AFNWC) systems including:
+ Strategic Mission Planning and Execution System (SMPES)
+ Mission Planning and Analysis System (MPAS)
+ Nuclear Planning and Execution System (NPES)
- Experience developing AI\-enabled automation for software engineering or DevSecOps workflows.
- Experience working with modern AI/ML frameworks such as TensorFlow, PyTorch, Scikit\-learn, or similar technologies.
- Experience supporting classified Department of Defense software development programs.
Who is Caribou Thunder?
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Caribou Thunder is a HUBZone\-certified small business providing advanced technical and engineering services to the U.S. Department of Defense and its mission partners.
35\+ states and 20\+ countries.
We've delivered trusted solutions for over two decades—strengthening national readiness across missions on land, undersea, in the air, and throughout LEO, MEO, GEO, and deep space.
Why Caribou Thunder?
========================
### TEAM THUNDER — Mission Focused. Delivery Proven. Ready to Serve.
- Employee Advocacy
- Mission Proven
- Global Reach
- Skilled Teams
- Modern Tools
- Empowering Culture
Our engineers and innovators ensure capability from sea floor to space frontier—delivering on time, maintaining compliance, and performing with precision in high\-consequence environments.
We specialize in Artificial Intelligence, Machine Learning, Data Science, Software Development, DevSecOps, Cybersecurity, Digital Engineering, and Modeling \& Simulation—disciplines powering the nation's most complex technical missions.
Employee Advocacy \+ Benefits
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Our people are the heart of Caribou Thunder. We invest in their growth, flexibility, and well\-being—knowing their success drives ours.
Benefits include:
- Premium Health, Dental \& Vision Insurance
- 401(k) with 6% Company Match
- Flexible PTO \& Work Schedule
- Education \& Certification Reimbursement
- Support for Military Leave
- Work–Life Balance \& Traditional Family Values
Your future, your flexibility, your well\-being—we invest in you.
Apply and let's connect.
Salary Context
This $90K-$110K range is in the lower quartile 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 Caribou Thunder, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $90K to $110K.
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.
Caribou Thunder AI Hiring
Caribou Thunder has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, US. Compensation range: $110K - $110K.
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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