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 young talent, 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.
