Great Question
AI customer research platform
AIEngineerIntern
Neural analysis suggests this role is
optimal for Entry candidates.
“AI Engineer Intern at Great Question. Skills: AI, LLM pipelines, agents, retrieval systems, evaluation frameworks, prompt optimization. Owning the full lifecycle of your project — from scoping through to shipped, production-ready code. Building and iterating on AI systems: LLM pipelines, agents, retrieval systems, evaluation frameworks, or prompt optimization”
What You'll Achieve.
ship real features; launching to real users; shipped, production-ready code
Industry & Context.
figure things out without waiting for instructions
What They're Looking For.
Must Have
Python/Javascript/Ruby, Git, testing, shipping real software, building things that actually work, self-starter with a hacker profile, curious and opinionated about where AI tooling is headed, communicate clearly
Nice to Have
repos, weekend builds, or side projects that show how you think about AI
What You'll Do.
Owning the full lifecycle of your project — from scoping through to shipped
production-ready code
Building and iterating on AI systems: LLM pipelines
evaluation frameworks
or prompt optimization
Writing production-quality
well documented code for people and agents that goes through the team's standard review process
Participating in daily standups
and architecture discussions alongside the engineering team
Driving SOTA AI practices and tool usage
Helping the team level up on AI — whether that's demoing a new approach in standup
pairing with engineers on AI-powered features
or just being the person who's always experimenting
Presenting your work to the team and leadership at the end of the program — what you built
How You'll Work.
Team & Collaboration
Participating in daily standups, code reviews, and architecture discussions alongside the engineering team; Pairing with engineers on AI-powered features
Communication Scope
communicate clearly; explain what you built and why
Process & Methodology
Owning the full lifecycle of your project — from scoping through to shipped, production-ready code
Full Job Description
ABOUT GREAT QUESTION: Great Question is the all-in-one AI customer research platform for understanding your customers. Our platform enables teams to recruit participants, run research, and share insights – all in one place. Backed by world-class investors and trusted by industry-leading teams like Gusto, Experian, Canva, and Brex, we’re building the future of customer research. We believe AI is a multiplier for creativity, speed, and scale. Everyone on the team is encouraged to lean into AI, not just to be more efficient, but to push boundaries and unlock new possibilities. Whether you’re writing, designing, coding, or analyzing, we expect you to explore how AI can elevate your craft. Our culture is built on high trust, high agency, and high emotional intelligence. If you’re looking for a place where your voice matters, where work is fun, and where you're empowered to do your best - join us in redefining user research 🚀 ABOUT THIS ROLE: We're hiring an AI-native Software Engineering intern for the summer. We're looking for an AI-native engineering intern to join us for the summer. You'll ship real features alongside the team — not a side project, not a sandbox. If you're already building with AI on your own time, this is a chance to do it at a company where it's the product. You'll own a real project from day one, working directly with our CTO as your mentor for the full three months, and launching to real users. You’ll work on interesting and challenging AI-focused projects related to some of the AI challenges our team is facing. A few examples: - Semantic search across tens of thousands of interview hours - Realtime agentic AI moderator - TTS, STT & Vision awareness - MCP tool structuring and prompt tuning - Setting up evals and quality measures across MCP tools and internal agents WHAT YOU'LL BE RESPONSIBLE FOR: - Owning the full lifecycle of your project — from scoping through to shipped, production-ready code - Building and iterating on AI systems: LLM pipelin
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