Member of Technical Staff interviews at research labs are unlike any other engineering loop. You are not evaluated on LeetCode fluency or generic system design patterns — you are expected to reason at the intersection of ML theory, large-scale systems, and empirical research methodology. Companies like OpenAI, Anthropic, NVIDIA, and Google Research evaluate MTS candidates on their ability to diagnose training failures, design principled ablation studies, and articulate the compute-quality tradeoffs that define frontier model development.
Member of Technical Staff uses AI-powered case practice to sharpen exactly these skills. Each session presents a live research diagnostic or system design scenario — a model quality gap analysis, a training instability investigation, a compute-optimal run design — and coaches you through structured decomposition, root cause analysis, and research methodology. Feedback is specific to MTS evaluation dimensions: scaling law reasoning, evaluation framework design, training hyperparameter judgment, and experiment structure.
How it works
- Practice research diagnostic cases modeled on real interview questions from OpenAI, Anthropic, NVIDIA, and Google Research
- Get AI-powered feedback on your training system design, ablation methodology, and scaling law reasoning
- Build skills across model evaluation, experiment design, ML theory, and research communication
- Track your progress across 20+ MTS competencies with adaptive difficulty
Why MTS interviews need dedicated prep
Generic software engineering or AI engineer prep does not prepare you for research lab MTS loops. MTS interviews go deep on domain-specific concerns that standard resources do not cover: how you think about compute-optimal training regimes, how you design ablation ladders that validate hypotheses without burning GPU budget, how you reason about evaluation contamination and benchmark validity, and how you communicate the difference between a training data issue and a hyperparameter issue given the same degraded benchmark result.
The AI coach pushes you on the quantitative specifics that distinguish strong MTS candidates. Not just "I would run more experiments" — but which tier of the experiment scale ladder you would start at, what proxy metrics you would use to validate the hypothesis before committing to a full run, and how you would interpret the diagnostic signal when validation loss looks fine but downstream benchmark performance does not. This level of reasoning separates candidates who get offers from those who get stuck in the loop.
Built for aspiring research engineers and scientists
Member of Technical Staff is designed for engineers and researchers targeting MTS roles at AI research labs, research engineer positions at frontier model companies, and senior IC roles at companies where ML infrastructure and training systems are core business capabilities. Whether you are transitioning from applied ML engineering into research lab roles or preparing for your next senior MTS or staff researcher position, structured case practice accelerates your readiness faster than self-study and paper reading alone.