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About This Role
About the Role: Turn your classroom learning into real-world impact with H&R Block’s Embark 2027 Summer Internship Program, open to students studying Computer/Data Science, Machine Learning, or Applied/Computational Mathematics. This 11-week paid internship offers meaningful, real-world experience that aligns with your field of study while making a valuable impact on our organization.
The Embark 2027 Summer Internship runs from May 17, 2027 – July 30, 2027.
You’ll work at our corporate headquarters in the heart of downtown Kansas City’s Power & Light District, where you’re just steps, or a streetcar ride, away from incredible restaurants, entertainment, and city energy. Inside our building, you’ll have access to amenities that make work-life balance easy, including an onsite Starbucks, a company dining hall with plenty of options, a free fitness center, and free parking!
Program Highlights:* $25/hour compensation
- Paid PTO during H&R Block’s annual Reboot Week (June 28 – July 2)
- Housing stipend provided for students whose permanent address is more than 50 miles from the KC metro
Throughout the 11-week internship, under the guidance of a leader, some of the internship responsibilities will include:* Design, build, and deploy machine learning models, including generative AI models, to address complex business problems.
- Clean, prepare, and analyze data for machine learning projects
- Tune and optimize models for accuracy, efficiency, and scalability in production environments.
- Collaborate with AI/ML and engineering teams to support model integration, deployment, and monitoring
- Explore emerging machine learning, deep learning, and Generative AI technologies
- Participate in code reviews, technical discussions, and collaborative problem-solving
- Document findings, share learnings, and present project outcomes and recommendations
Throughout the 11-week internship, as a part of the Embark program, interns will also participate in workshops and events that align with the program's four pillars:
Professional Development: includes the basics for a professional role, everything from using Microsoft Office to setting up a Teams meeting and giving a presentation.
Giveback: involves participating in Make Every Block Better community service events. The goal is to teach the importance of community, make an impact, and provide bonding time for teams.
Networking: entails meeting various executives with offices at H&R Block’s headquarters, having lunch with directors and vice presidents, spending time with a mentor and getting to know people beyond their assigned department.
Connected Culture: is meant to be fun and teach the importance of working hard and playing hard. H&R Block might take the interns to a Kansas City Royals game or facilitate other competitions meant to be fun or instill teamwork. Required Qualifications: Education:
- High school diploma or equivalent
- Seeking to obtain a degree in Information Computer/Data Science, Machine Learning, Applied/Computational Mathematics, or some similar discipline with a GPA of 3.0 or above
- Ability to work as part of a diverse team and independently
- Highly motivated, organized, reliable, and detail-oriented
- Strong written and verbal communication skills
- Quick learner and not afraid to work with new computer-based programs and databases
- Ability to be effective and contribute in a fast-paced corporate environment
Work Experience:* No previous work experience necessary
Preferred Qualifications:
- Understanding of machine learning fundamentals, including model training, testing, evaluation, and optimization
- Exposure to ML frameworks or libraries such as PyTorch, TensorFlow, scikit-learn, or similar tools
- Familiarity with Python and machine learning fundamentals
- Exposure to PyTorch, TensorFlow, scikit-learn, or similar ML frameworks
- Familiarity with SQL, data analysis, and data preparation
- Exposure to Microsoft Azure, Azure Machine Learning, or other cloud-based AI/ML technologies
- Interest in Generative AI, LLMs, embeddings, vector search, and AI agents
- Coursework or project experience involving AI, machine learning, data science, or Generative AI is a plus
Why work for us: At H&R Block, our purpose is simple: to provide help and inspire confidence in our clients and communities everywhere. For more than 70 years, we’ve led the tax industry and we continue to evolve through our Block Next strategy, blending human expertise with digital innovation.
That same commitment to care extends to our associates. When you join us, you become part of a High Performing, Connected Culture built on strong relationships, continuous learning, and a shared commitment to making a meaningful impact. We invest in your well-being through competitive pay, comprehensive benefits, and support for life both in and outside of work.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At H&R Block, 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 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
H&R Block AI Hiring
H&R Block 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% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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