Interested in this AI/ML Engineer role at CustomBoxes.io?
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About This Role
Role Overview
CustomBoxes.io is looking for an Applied AI Software and Automation Intern to help us build practical AI\-powered tools, automations, and prototypes across eCommerce, branding, marketing, and operations.
This is not a traditional software engineering internship focused only on writing code from scratch. We are looking for someone who is highly fluent in modern AI tools and can combine prompting, APIs, automation platforms, lightweight software development, and creative problem\-solving to build useful things quickly.
A major focus of this role will be helping us improve and expand our approved Shopify app, while also supporting internal AI workflows that make our team faster, more creative, and more effective
You will work closely with the founder and team members to test ideas, build prototypes, connect tools, and turn business problems into working solutions
What You’ll Work On
- Help improve and extend our approved Shopify app
- Build AI\-powered workflows that pull in website, product, and brand data
- Support tools that identify and extract brand assets such as logos, colors, and visual patterns from customer websites
- Help prototype packaging recommendation tools using Shopify product and sales data
- Support AI\-assisted workflows for generating graphics, content, packaging concepts, and marketing assets
- Connect AI tools, APIs, and automation systems to reduce manual work across the company
- Build lightweight internal tools and experiments that improve speed, accuracy, and creativity
- Test multiple AI tools and platforms to determine which ones are most effective for a given use case
- Document workflows, learnings, and recommendations so successful experiments can be reused and scaled
What We Are Actually Looking For
- Someone who is highly fluent in current AI tools, not just curious about them
- Someone who can move from idea to prototype quickly
- Someone who is comfortable mixing software, automation, prompting, APIs, and experimentation
- Someone who likes building real things that solve business problems
- Someone who is resourceful, practical, and comfortable working in a fast\-moving environment
Must\-Have Qualifications
- Strong hands\-on experience with modern AI tools for research, writing, analysis, ideation, design support, and workflow execution (list below)
- Ability to build working prototypes using JavaScript, Python, or similar tools
- Comfort with APIs, JSON, webhooks, and lightweight integrations
- Familiarity with Shopify, eCommerce workflows, or app ecosystems
- Experience building or supporting software, automations, or internal tools
- Ability to use AI tools to generate useful outputs across content, research, workflow automation, design support, or operational problem\-solving
- Strong problem\-solving skills and ability to work independently
- Good judgment on when to use custom code, when to use automation, and when to use off\-the\-shelf AI tools
Preferred Qualifications
- Experience with Shopify app development or Shopify APIs
- Familiarity with React, Node.js, Tailwind, and modern app development workflows
- Experience with workflow and agent tools such as MindStudio or similar automation platforms
- Familiarity with cloud backends such as Supabase, Firebase, or AWS
- Experience working with image generation, image processing, or creative AI tools
- Experience with GitHub and collaborative development workflows
- Exposure to building prototypes that combine business logic, automation, and user\-facing workflows
- Interest in eCommerce, branding, packaging, or customer experience
Tools and AI Technologies
Preferred experience with modern AI development tools such as Base44, Lovable, v0, Bolt, Replit Agent, Firebase Studio, Cursor, Windsurf, Claude Code, OpenAI Codex, GitHub Copilot, ChatGPT, Claude, Gemini, Perplexity, Zapier, Make, n8n, MindStudio, Midjourney, Canva AI, Adobe Firefly, Runway, Shopify APIs, React, Node.js, and related platforms. We do not expect expertise in every platform, but we are looking for someone who is fluent in several, learns quickly, and has a demonstrated ability to combine AI, software, and automation into useful business solutions
What Matters Most
- Ability to use AI tools to build real software, automations, prototypes, and workflows
- Ability to combine multiple tools into a practical solution
- Speed of execution, curiosity, and willingness to test, learn, and ship quickly
- Strong judgment on which tools are worth using and which are not
What Success Looks Like
- You help ship meaningful improvements to our Shopify app
- You build useful internal AI workflows and prototypes
- You identify high\-value tools and eliminate low\-value ones
- You help turn rough ideas into practical outputs quickly
- You make our team faster by reducing manual work and improving creative and operational workflows
Important Note
This role is best suited for someone who is part AI builder, part automation thinker, and part technical problem\-solver. We are not looking for a pure machine learning researcher. We are looking for someone who can use today’s best AI tools, software, and automation methods to help us create useful, impressive, business\-relevant solutions quickly
Type of person that will fit in well
- Independent and goal orientated
- Driven to success (bonus opportunities on productivity)
- Problem solving and likes looking at numbers to improve results
- Intellectually curious
- Easy to get along with
- Passion for helping small business, non\-profits and the environment (any or all)
- Attention to detail (mistakes cost our SMB clients a lot)
- Culture Page: https://customboxes.io/pages/working\-at\-customboxes\-io\-people\-culture
This internship is designed for individuals eager to learn and grow within the marketing field while making meaningful contributions to our team.
Job Types: Part\-time, Contract, Temporary
Pay: $15\.00 \- $20\.00 per hour
Benefits:
- Flexible schedule
Experience:
- AI: 1 year (Required)
- AI models: 1 year (Preferred)
Work Location: Remote
Salary Context
This $31K-$41K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At CustomBoxes.io, 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 $166,983 based on 13,781 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880. This role's midpoint ($36K) sits 78% below the category median. Disclosed range: $31K to $41K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
CustomBoxes.io AI Hiring
CustomBoxes.io has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $41K - $41K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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