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About This Role
AI Engineering Intern — AR5 Labs
Remote \| Internship
ABOUT AR5 LABS
AR5 Labs builds AI\-powered automation tools for data analytics. Our flagship product, PlotStudio AI, is a desktop analytics platform where specialized AI agents collaborate through a multi\-stage pipeline to deliver the kind of analysis a senior data scientist would produce, complete with code, visualizations, statistical rigor, and executive\-grade narrative. Think Cursor, but for data analytics.
Product: https://www.plotstudio.ai/
LinkedIn: https://www.linkedin.com/company/ar5\-labs/
ABOUT THE ROLE
We're looking for an AI Engineering Intern to help build and improve the AI agent pipeline behind PlotStudio AI.
This is not a research internship. You'll work on production AI systems: prompt engineering, agent orchestration, model evaluation, and the infrastructure that makes multi\-agent workflows reliable. You'll see how AI agents are actually built, tested, and shipped to real users.
You'll work directly with the founder and the AI/ML team. The codebase is small enough that your contributions will ship to production quickly, and complex enough that you'll learn how a real multi\-agent system works from the inside.
If you're a student who wants to build AI systems, not just study them, this is the role.
RESPONSIBILITIES
\- Help design, test, and iterate on prompts for PlotStudio's multi\-agent pipeline (routing, planning, coding, interpretation agents)
\- Build evaluation scripts to benchmark agent output quality across different datasets and question types
\- Run experiments comparing model performance (GPT\-4\.1, GPT\-5\.1, Claude, Gemini) on specific analytical tasks
\- Help improve agent self\-correction logic: how the system detects errors, retries, and adjusts its approach
\- Work on domain skill modules that inject specialized knowledge (finance, healthcare, econometrics) into the agent pipeline
\- Build internal tooling for tracking agent accuracy, token usage, and failure modes
\- Help extend the agent orchestration layer (built with LangGraph) to support new workflow types
\- Document prompt versions, experiment results, and engineering decisions as you go
QUALIFICATIONS
Required:
\- Currently pursuing a degree in Computer Science, Data Science, AI/ML, or a related field (rising sophomore, junior, or senior)
\- Comfortable writing Python. You should be able to read, write, and debug code independently
\- Basic understanding of how LLMs work: prompts, tokens, context windows, temperature
\- Genuine interest in AI agents, not just chatbots. You should have an opinion on why single\-prompt AI falls short for complex tasks
\- Willingness to experiment, break things, and iterate quickly
\- Self\-directed and comfortable asking questions when you're stuck
\- Able to commit 15\-20 hours per week
Nice to have:
\- Experience using the OpenAI API, Anthropic API, or any LLM SDK
\- Familiarity with LangChain, LangGraph, CrewAI, or any agent framework
\- Personal projects involving LLMs, prompt engineering, or AI agents
\- Coursework in machine learning, NLP, or statistics
\- Familiarity with Git and version control
\- Experience with FastAPI, Flask, or any Python backend framework
\- Understanding of statistical methods (regression, hypothesis testing, time series)
WHAT YOU'LL LEARN
\- How a production multi\-agent system is built: routing, planning, execution, self\-correction, and interpretation agents working in sequence
\- Prompt engineering at a level most people never see: versioned prompts, domain\-aware injection, structured output parsing, and quality gates between agents
\- How to evaluate AI systems beyond vibes: building benchmarks, measuring accuracy, tracking regressions across prompt versions
\- How to ship AI to real users in a small, fast\-moving team
WHY THIS INTERNSHIP
\- You'll work on a real multi\-agent AI system, not a tutorial project
\- Small team means high visibility and direct mentorship from the founder
\- Your work will ship to production and be used by real customers
\- You'll have something concrete for your resume: "I helped build and evaluate the AI agent pipeline for a production analytics platform"
MUST HAVE
We are across the United States and Canada. Must be a citizen of either country.
HOW TO APPLY
Follow our LinkedIn page and DM us directly, or email us:
1\. A short intro about yourself
2\. Your resume or LinkedIn profile
3\. What year you're in and what you're studying
4\. Why you want to work at AR5 Labs
Email: plotstudio@ar5labs.com
LinkedIn: https://www.linkedin.com/company/ar5\-labs/
Pay: $20\.00 \- $45\.00 per hour
Work Location: Remote
Salary Context
This $41K-$93K range is below the median 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 AR5 Labs, 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 ($67K) sits 60% below the category median. Disclosed range: $41K to $93K.
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.
AR5 Labs AI Hiring
AR5 Labs has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $93K - $134K.
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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