Lilly Tech Systems
AI Research Scientist Interview Prep
Prepare for research scientist interviews at top AI labs and companies. Real questions covering paper discussions, mathematical depth, research proposals, coding for research, and research taste — the complete preparation guide for interviews at Google DeepMind, Meta FAIR, OpenAI, Anthropic, Microsoft Research, and leading AI research organizations in 2024–2026.
Your Learning Path
Understand the research scientist interview process, master paper discussions and mathematical depth, learn to pitch research proposals, and develop the research taste that top labs demand.
1. Research Scientist Interview Overview
How research interviews differ from engineering, interview stages at top labs, what hiring committees look for, and PhD vs industry research paths.
2. Paper Discussion Round
How to present your own papers, discuss others' work, demonstrate critical analysis skills, and 5 example paper discussion frameworks used at top labs.
3. Research Proposal Questions
How to pitch research ideas, evaluate feasibility and novelty, estimate resources, and 3 example research proposals with detailed breakdowns.
4. Mathematical Depth Questions
Linear algebra, probability, optimization, and information theory questions with detailed solutions — the math researchers are expected to know cold.
5. Research Coding Round
Implementing papers from scratch, PyTorch coding questions, experiment design, reproducibility practices, and code quality standards for research.
6. Research Taste & Vision
Identifying important problems, connecting disparate fields, developing long-term research vision, and navigating uncertainty in open-ended research.
7. Preparation Strategy
Paper reading list, research portfolio building, presentation tips, and FAQ accordion for common research interview concerns.
What You'll Learn
By the end of this course, you will be able to:
Present & Discuss Papers
Clearly present your research contributions, critically analyze others' papers, identify limitations, and propose extensions — the core skill that separates strong research candidates from average ones.
Pitch Research Proposals
Formulate compelling research proposals, assess novelty and feasibility, estimate compute and data requirements, and defend your ideas against challenging questions from senior researchers.
Demonstrate Mathematical Depth
Solve linear algebra, probability, optimization, and information theory problems at the whiteboard with the rigor and fluency expected of PhD-level researchers at top AI labs.
Code Like a Researcher
Implement papers from scratch in PyTorch, design rigorous experiments, write reproducible code, and demonstrate the software engineering skills that accelerate research velocity.