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About This Role
Build the Future of Business Software with AI
Host Merchant Services is looking for a creative, technology\-minded individual who enjoys building things, improving user experiences, and leveraging AI to solve real business problems.
This is not a traditional software developer position.
We are not looking for someone who has spent 10 years writing code.
Instead, we're looking for someone with strong attention to detail, a good eye for design, and a passion for using modern AI tools to build useful applications, workflows, and customer experiences.
If you constantly notice bad user interfaces, enjoy making things look polished, and find yourself experimenting with tools like Claude Code, Cursor, ChatGPT, Figma, or other emerging technologies, you may be exactly who we're looking for.
What You'll Do
You'll work directly with company leadership and our technology team to improve existing software and create new solutions for our customers.
Projects may include:
- Designing and improving software user interfaces
- Building prototypes using AI\-assisted development tools
- Working with Claude Code, Cursor, ChatGPT, and other AI platforms
- Creating dashboards, portals, workflows, and internal tools
- Improving customer onboarding experiences
- Testing new AI technologies and identifying practical business uses
- Reviewing software for usability, consistency, and accuracy
- Creating mockups and user experience improvements
- Working with developers to turn ideas into production\-ready solutions
- Helping shape the future of our software platforms
What Makes Someone Successful in This Role?
The best candidate is someone who:
- Notices small details others miss
- Has a strong sense of design and user experience
- Thinks critically about how software should work
- Enjoys solving business problems
- Learns new technology quickly
- Is comfortable experimenting and iterating
- Takes pride in producing polished work
- Can balance creativity with accuracy
We can teach architecture, business processes, payment technology, and industry\-specific knowledge.
What we cannot easily teach is taste, attention to detail, and a desire to make products better.
Skills We Like to See
- Experience with AI tools such as Claude Code, Cursor, ChatGPT, Gemini, or similar platforms
- Basic understanding of HTML, CSS, JavaScript, or software development concepts
- Experience with Figma or other design tools is a plus
- Personal projects, websites, apps, or automations
- Strong written communication skills
- Ability to think from the user's perspective
- Interest in product design, software, AI, or automation
A degree is helpful but not required.
We care more about what you've built and how you think than where you went to school.
Technologies You May Work With
- Claude Code
- Cursor
- ChatGPT
- OpenAI APIs
- Anthropic APIs
- Laravel
- PHP
- JavaScript
- HTML/CSS
- Figma
- AI Agents
- Automation Platforms
- Payment Technology Systems
Why Join Host Merchant Services?
We're a growing FinTech company that develops our own software platforms while actively investing in AI and automation.
You'll have the opportunity to:
- Work directly with decision makers
- Learn emerging AI technologies
- Gain real\-world product development experience
- Build a portfolio of meaningful projects
- Help shape products used by businesses across the country
- Develop highly marketable AI and product design skills
If you're the type of person who sees a piece of software and immediately starts thinking about how it could be improved, we'd love to talk with you.
Pay: From $20\.00 per hour
Benefits:
- Dental insurance
- Health insurance
- Vision insurance
Work Location: In person
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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Host Merchant Services, 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 $180,000 based on 12,398 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Host Merchant Services AI Hiring
Host Merchant Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Newark, DE, US.
Location Context
Across all AI roles, 15% (593 positions) offer remote work, while 3,349 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,103 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), AI Software Engineer (279). 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $290,000 median, while Prompt Engineer roles sit at $140,000. 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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