Plan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab
Overview
What this challenge is about.
You will produce (1) a 6-page survey of four PEFT methods (LoRA, adapters, prefix tuning, IA3) with their strengths, weaknesses, and parameter footprints, (2) a one-page decision tree that picks one method based on dataset size, downstream task type, and serving constraints, (3) a worked example: pick a small open base model (e.g., Llama-3.2-1B), fine-tune it with two of the four methods on a public classification task, and compare task accuracy + adapter size. Deliver as a research-style writeup the lab can publish internally.
The Brief
What you'll do, and what you'll demonstrate.
Produce an internal decision framework that helps applied-research teams pick a PEFT method without re-running the comparison every time.
Earning criteria — what you'll demonstrate
- Compare modern parameter-efficient fine-tuning techniques
- Build a decision framework that captures the relevant axes of choice
- Run a small but rigorous PEFT comparison on a public task
- Communicate a survey + decision framework to an internal research audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Research Scientist
Producing canonical internal references (survey + decision framework + worked example) is exactly the research-scientist's contribution at any large applied-AI lab.
This challenge sharpens
- parameter-efficient-fine-tuning
- experiment-design
- transfer-learning
ML Researcher
Rigorous comparative surveys of fine-tuning techniques with worked examples are the ML-researcher's headline portfolio piece.
This challenge sharpens
- fine-tuning
- transformer
- parameter-efficient-fine-tuning
Applied AI Scientist
Turning research literature into an internal decision tree product teams will actually use is the applied-AI-scientist's day-job at any big-tech lab.
This challenge sharpens
- parameter-efficient-fine-tuning
- transfer-learning
- experiment-design