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About This Role
Union Home Mortgage’s L.E.A.D Internship Program’s goal is to provide a fun, interesting, and real\-world environment for our interns to Learn about the industry, be Educated by Sr. Leadership and their peers, Achieve their personal goals and Develop their skills and knowledge base. We pride ourselves in providing innovative programs for our interns in order for them to learn and grow they progress through their careers. Some of the programs we offer include: shadowing, mentoring, professional development, group projects and we even take our interns on corporate outings! Our internship program gives students a chance to meet new people, gain more experience, and learn from the best in the business! Our interns are treated like full\-time Partners who work 40 hours a week during the 3\-month summer program, are compensated, and based out of headquarters in Strongsville, Ohio.
An AI Engineer Intern will support the research, development, and evaluation of artificial intelligence solutions designed to improve mortgage operations, including loan origination, servicing, workflow efficiency, and customer experiences.
In this role, you will apply foundational knowledge of machine learning, neural networks, Transformer architectures, large language models (LLMs), retrieval\-augmented generation (RAG), and agentic AI systems to real\-world business challenges. Working alongside experienced engineers and business stakeholders, you will research technical approaches, develop prototypes, test and evaluate AI systems, and help determine where AI can meaningfully improve processes and reduce manual effort.
DUTIES \& RESPONSIBILITIES* Research \& Develop AI Solutions – Explore and prototype AI/ML solutions that improve business processes and customer experiences
- Automate Repetitive Tasks – Identify opportunities to apply AI and automation to reduce manual effort
- Build \& Improve AI Workflows – Assist in developing LLM, RAG, agentic, and traditional machine learning workflows
- Test \& Evaluate Solutions – Evaluate AI outputs for accuracy, reliability, performance, and appropriate business use
- Support Responsible AI – Assist with safeguards, testing, documentation, and human\-in\-the\-loop processes
- Collaborate Across Teams – Work with technical and business stakeholders to understand problems and develop appropriate solutions
- Research Emerging Technology – Stay current with developments in AI, machine learning, LLMs, and related technologies and evaluate their potential application
- Basic to intermediate programming proficiency in Python and familiarity with software development concepts
- Foundational understanding of machine learning, including supervised/unsupervised learning, training and validation, loss functions, optimization, overfitting, generalization, and model evaluation
- Foundational understanding of neural networks and deep learning, including layers, weights, activation functions, forward propagation, backpropagation, and gradient\-based learning
- Ability to explain the fundamentals of Transformer architectures, including attention/self\-attention, tokens, embeddings, context windows, and autoregressive generation
- Understanding of tokenization and embeddings, including how text is represented for LLMs and how embeddings support semantic search and retrieval
- Familiarity with large language models (LLMs), including pretraining, inference, prompting/context engineering, tool calling, model limitations, hallucinations, and evaluation
- Familiarity with RAG, fine\-tuning, and model adaptation, including the high\-level differences between prompting, retrieval, and fine\-tuning approaches
- Interest in AI agents and agentic workflows, including tool use, state, memory, routing, and orchestration; exposure to LangGraph, LangChain, or similar frameworks is a plus
- Exposure to AI/ML libraries or frameworks such as PyTorch, TensorFlow, Hugging Face, scikit\-learn, NumPy, or pandas is preferred
- Strong analytical, problem\-solving, communication, and technical research skills with the ability to explain how and why an AI system works, not simply how to use it
EDUCATION \& EXPERIENCE
- Currently pursuing a bachelor's or graduate degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computer Engineering, Software Engineering, Mathematics, Statistics, or a related technical discipline
- Relevant coursework in artificial intelligence, machine learning, deep learning, natural language processing, algorithms, statistics, probability, linear algebra, or calculus is preferred
- Foundational understanding of the mathematical and computational principles underlying modern AI and machine learning
- Experience through coursework, research, independent study, personal projects, hackathons, open\-source contributions, or previous internships is welcomed
- Demonstrated ability to research technical concepts, experiment with different approaches, evaluate results, and clearly communicate findings
- A GitHub profile, AI/ML project, research project, technical portfolio, or similar demonstration of technical work is preferred
- Prior professional AI engineering or machine learning experience is not required
Applications are accepted on a rolling basis and positions are open until filled (this may be prior to the job posting expiration date).
At UHM, we understand diversity comes in many different forms. It’s our commitment to improve inclusion in the workplace through programs and policies that establish a positive and inclusive environment where every Partner, regardless of their background, can grow and excel. We value diversity, educate on equity, and create inclusive partner opportunities to ensure that you know \#UBelongAtUHM!
This employer participates in E\-Verify. If hired, the employer will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the U.S.
Union Home Mortgage Corp. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
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 Union Home Mortgage, 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. Entry-level AI roles across all categories have a median of $110,000.
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.
Union Home Mortgage AI Hiring
Union Home Mortgage has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Strongsville, OH, US.
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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