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About This Role
AI Engineer Intern
Division: Business Technology – Data & AI
Location: Hybrid | Omaha, NE (Tower)
Reports to: Chief Data Officer
Compensation Band: 3
About WoodmenLife’s Data & AI Team
Our Data & AI organization is made up of 16 talented professionals—including Data Engineers, Data Analysts, Data Architects, and AI Engineers—working together to build modern, scalable data and AI solutions that move WoodmenLife forward. As an AI Engineer Intern, you’ll be part of a collaborative team building cutting edge automation and agentic AI solutions that directly support major business operations.
Why You’ll Love This Internship
Work directly with frontier AI technologies
Build real solutions that go into production
Join a supportive, high-energy team pushing innovation forward
Role Overview
The AI Engineer Intern will work closely with experienced AI Engineers to design, develop, test, and document AI driven solutions using Microsoft’s Azure AI ecosystem. This role is ideal for a student or early career technologist who wants hands on experience with frontier AI technologies and real world production systems.
What You’ll Do (Daily Responsibilities)
Participate in daily stand ups and collaborate with teammates on project priorities
Work with senior AI Engineers to build agentic and automation solutions
Assist in testing and validating technologies being developed for production
Create documentation and contribute to the team’s internal knowledge base
Partner with business stakeholders to understand processes and identify opportunities for automation
Tools & Technologies You’ll Use
Microsoft 365
Copilot Studio
Microsoft Foundry
Claude Code
Azure AI ecosystem
Projects You May Support
You’ll gain hands-on experience contributing to high impact initiatives such as:
Custom Copilots for HR
Automation solutions for Customer Service
AI powered tools to support Sales operations
Career Growth Opportunities
This internship provides a strong foundation for future full time roles in AI engineering. Top performing interns may have the opportunity to transition into an AI Engineer I role.
What We’re Looking For
Interest in AI, automation, data engineering, or software development
Curiosity, adaptability, and willingness to learn
Ability to collaborate with technical and business partners
Strong problem solving and communication skills
Team Culture & Leadership
You’ll report to our Chief Data Officer, who leads with a collaborative management style and values open communication. The team prides itself on being supportive, innovative, and fun—always exploring new ways to leverage AI to reinvent how work gets done.
30/60/90 Day Expectations
First 30 Days:
Onboard to tools, platforms, and team processes
Pair closely with an experienced AI Engineer mentor
60 Days:
Begin contributing to POC enhancements and small components of production solutions
90 Days:
Support moving POCs into production-ready workflows
Demonstrate ownership of assigned tasks within team projects
WoodmenLife offers a competitive compensation package and a comprehensive benefits package (https://www.woodmenlife.org/careers/home-office/benefits/). As part of WoodmenLife’s employment process, candidates will be required to complete a criminal background check, credit check (where required for position), Fingerprint check (where required for position), drug screen and reference checks. Any offer of employment will be contingent upon successfully passing the above.
WoodmenLife is committed to excellence in diversity by creating an inclusive work environment that values and respects all individuals. We welcome and embrace associates, regardless of background and beliefs. WoodmenLife respects every associate’s unique perspective and contribution. We are committed to creating an inclusive environment that values differences, and creates opportunities for growth, leadership and service. This commitment includes providing equal opportunity in recruitment, employment and promotion, training and community outreach. WoodmenLife is also dedicated to strengthening the communities in which its employees live.
APPLICANTS WITH DISABILITIES SHOULD ADVISE THE HUMAN RESOURCES DEPARTMENT AT THE TIME OF APPLICATION IF SPECIAL ACCOMMODATIONS ARE NEEDED.
Woodmen of the World Life Insurance Society (WoodmenLife) is an equal opportunity employer.
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 3,897 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At WoodmenLife, 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 $154,000 based on 8,743 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $85,000.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
WoodmenLife AI Hiring
WoodmenLife has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, US.
Location Context
Across all AI roles, 16% (615 positions) offer remote work, while 3,251 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 3,897 open positions tracked in our dataset. By seniority: 111 entry-level, 1,958 mid-level, 1,413 senior, and 415 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (615 positions). The remaining 3,251 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 3,897 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,733), Data Scientist (273), AI Software Engineer (271). 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 (111) are outnumbered by mid-level (1,958) and senior (1,413) 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 415 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (615 positions), with 3,251 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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 (2,064 postings), Aws (1,085 postings), Azure (867 postings), Rag (865 postings), Gcp (697 postings), Pytorch (650 postings), Prompt Engineering (597 postings), Kubernetes (499 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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