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About This Role
About us
HMH is a leading provider of drilling solutions, offering a wide range of products and services that are designed to be the safest and most efficient in the industry. Apart from our expertise in land and offshore operations, we are continuously expanding our knowledge within subsea mining, geothermal, onshore and offshore construction, as well as offshore wind industries. With offices in 16 countries across five continents, HMH maintains a strong global presence.
HMH is a frontrunner in developing and providing automation and digital solutions for our drilling customers to support their endeavor to improve efficiency and environmental footprint. Equipped with its brilliant team of engineers, HMH is committed to actively exploring opportunities in other industries. For us, this means new opportunities and challenges that we need creativity and great minds to solve in our efforts to innovate our future.
Do you want to join our team?
At HMH we value our employees. We offer exciting job opportunities that will give you the opportunity to grow in your role and give you the professional development you deserve. In addition to competitive pay and benefits, you will join a casual and inclusive work environment. Our environment is based on respect and having a good day at work, so you can expect to join a knowledgeable, global team who help each other to succeed.
The IT Intern will be responsible for:
- Assist in gathering business requirements and documenting process steps, decision points, exceptions, and expected outcomes for AI-enabled automation opportunities.
- Participate in the development, testing, and evaluation of AI agents and automated workflows under the guidance of experienced team members.
- Assist with connecting approved data sources, such as databases, data warehouses, files, APIs, and business applications, to support workflow decisions.
- Help develop workflow logic that combines defined business rules with AI-generated recommendations or decisions.
- Support the implementation of human-review and approval steps when a workflow requires validation, correction, escalation, or authorization.
- Assist with configuring approved automated actions in destination systems through APIs, integration services, or workflow tools.
- Test workflow outputs, decision paths, data validation, error handling, and exception scenarios, and document the results.
- Help monitor prototypes and solutions, identify issues or improvement opportunities, and work with the team to refine performance and reliability.
- Assist in documenting solution designs, prompts, decision rules, integrations, test cases, operating procedures, and lessons learned.
- Stay up to date with developments in artificial intelligence, agentic systems, workflow automation, and responsible AI practices.
The Intern will also learn about the following:
- AI agents, generative AI, large language models, and prompt design
- Agent orchestration, tool and function calling, and structured outputs
- Business-process analysis and intelligent workflow automation
- Data retrieval from databases, warehouses, files, APIs, and enterprise applications
- Decision workflows with and without human review
- API integration and authorized actions in destination systems
- Testing, evaluation, monitoring, exception handling, and auditability
- Responsible AI, information security, privacy, access control, and documentation
- Agile software development, version control, code review, and project collaboration
Skills/Requirements
- Currently enrolled and working toward completion of Bachelor or Graduate degree from an accredited university or college in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems, Computer Engineering, or a related field
- Minimum GPA of 3.0
- Strong interest in AI, automation, software development, and business-process improvement, with a desire to learn and grow
- Basic programming knowledge in Python or a similar language
- Basic understanding of databases, SQL, APIs, JSON, or software integration concepts
- Familiarity with generative AI, large language models, prompt engineering, machine learning, or workflow automation through coursework, personal projects, or prior experience is preferred
- Excellent problem-solving and analytical skills
- Strong written and spoken communication and interpersonal skills
- Ability to work independently and as part of a team
- Attention to detail and ability to prioritize tasks
- Professionalism and a strong work ethic
Additional Information
- Must be legally authorized to work in the United States on a full-time basis, now or in the future, without employer sponsorship for employment visa status.
- No relocation assistance provided.
- Internship is 12 weeks, May 24 - August 13, 2027
Location
Houston, TX
*This position is not open to visa sponsorship*
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 HMH, 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. Entry-level AI roles across all categories have a median of $110,408.
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.
HMH AI Hiring
HMH has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, 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
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