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Opportunities · Part-time opportunity

AI Engineer Intern

DiligenceVault · New York City, NY

Applies on the employer's siteNew to UTern today76 days left to apply≈$82-$100/hr

Role overview

What you'll own

  • You will learn how fund diligence and investment research actually work, then help solve problems involving unstructured documents, semantic search, RAG, workflow automation, market intelligence, and AI evaluation.
  • You will be expected to move quickly from idea to proof of concept, test against real workflows, learn from failures, and improve what works.
  • Build automations for diligence processes
  • Build and improve semantic search, multi-model architecture, RAG, document intelligence, and AI agents

Requirements and eligibility

  • Pursuing a Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Engineering, or a related field
  • Strong Python skills and solid software engineering fundamentals
  • Familiarity with LLMs, embeddings, RAG, NLP, or modern AI frameworks
  • Comfortable working with messy, real-world data

Dates for this one

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Posted

Sep 11, 2026

Applications close

Dec 10, 2026

76 days left

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Read the full listing context

Artificial Intelligence Engineer Intern Location: New York City | Internship Duration: 3 months Part-time (Less than 30 hours a week) | Work Mode: Hybrid, 8 hours/week in office About DiligenceVault DiligenceVault is an AI-powered diligence and decision intelligence platform used by allocators, investors and asset managers.

We help diligence teams turn messy documents, fragmented data, market intelligence, and manual workflows into intelligent automations, faster analysis, and better decisions.

The Role We are looking for a curious, hands-on AI Engineer Intern to help build and test practical AI capabilities inside a scaled enterprise product.

You will learn how fund diligence and investment research actually work, then help solve problems involving unstructured documents, semantic search, RAG, workflow automation, market intelligence, and AI evaluation.

This is a build-and-iterate role. You will be expected to move quickly from idea to proof of concept, test against real workflows, learn from failures, and improve what works.

What You’ll Work On • Build automations for diligence processes • Solve hard and messy data problems • Embed market, regulatory, and public intelligence into product workflows • Build and improve semantic search, multi-model architecture, RAG, document intelligence, and AI agents • Rapidly prototype new AI ideas and help move successful PoCs toward production What We’re Looking For • Pursuing a Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Engineering, or a related field • Strong Python skills and solid software engineering fundamentals • Familiarity with LLMs, embeddings, RAG, NLP, or modern AI frameworks • Comfortable working with messy, real-world data • Strong problem-solving skills, user empathy, and a bias toward experimentation • Curious about financial markets, investment research, and how technology can transform complex workflows Prior investment-industry experience is not required.

Curiosity and the ability to learn quickly are. What You’ll Get • Hands-on experience building AI for a production B2B SaaS platform • Exposure to institutional investing, fund diligence, and market intelligence • The opportunity to work on problems at the intersection of data, workflow, and AI at scale • Direct collaboration with AI, engineering, product, and industry teams • Internship compensation - Ranging USD 18 to USD 22 per hour Note: We do not provide work visa sponsorship When applying, please send us: • Something you’ve built with AI - a project, prototype, research, or other work using AI technologies • Your GitHub profile • Three bullets on why this internship interests you Next step in Interview Process: You'll be asked to complete a short assignment designed to assess your AI, product thinking, and engineering approach.

The assignment is expected to take approximately 2-4 hours to complete.

Verified by UTern

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