About the Role

This internship is part of Tech Seeds 2026 — our program to identify and develop AI-native young talent. As an Applied Data Scientist intern, you will take on a hands-on builder role at the intersection of AI research and production systems, working alongside full-time algorithm engineers on LLM applications and AI system architecture from day one.

This is not a shadowing role. You will own deliverables, run experiments, and ship code that matters.

What You'll Do

• Support the design and development of LLM-powered pipelines and AI application components

• Build and test prompt engineering strategies, retrieval-augmented generation (RAG) setups, or agent workflows

• Assist in model evaluation — designing metrics, running benchmarks, and interpreting results

• Analyze system performance and identify opportunities for improvement in latency, accuracy, or cost

• Use AI coding tools as a core part of your workflow — not as a shortcut, but as a force multiplier

• Collaborate with senior engineers and researchers to understand production AI system architecture

What We're Looking For

• Currently pursuing Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related field

• Expected graduation in 2026 or 2027

• Solid Python programming skills and comfort with data manipulation (Pandas, NumPy)

• Basic understanding of machine learning concepts and at least one deep learning framework (PyTorch preferred)

• Curiosity about LLMs and generative AI — hands-on experimentation is a strong signal

• Ability to break down complex problems and work through them systematically

• Available for a 6-month full-time commitment

Bonus Points

• Personal projects, coursework, or competition experience involving LLMs or generative AI

• Familiarity with tools like LangChain, OpenAI API, or Hugging Face

• Experience with Git and collaborative development workflows

What Makes You a Fit for Tech Seeds 2026

You've already been experimenting with AI tools on your own — not because you had to, but because you were curious. You learn fast and want to build things that work in the real world.