Prepare for your Google Research MTS interview

ML research methodology and large-scale training system design cases calibrated to Google Research and DeepMind’s research-scientist, publish-or-perish MTS culture.

Socratify AI

The Interview

What Google is looking for

Research Interview

ML Research Methodology

01Hypothesis-Driven Experiment Design

02Ablation Study Structure and Scope

03Benchmark Selection and Validity

04Scientific Communication and Reproducibility

Systems Interview

Large-Scale ML Systems

01TPU/GPU Training Infrastructure

02Distributed Training Architecture (Pathways)

03Mixture-of-Experts System Design

04Long-Context Attention Optimization

Behavioral Interview

01Research Impact and Prioritization

02Intellectual Curiosity and Depth

03Collaborative Research Culture

04Long-Term Scientific Vision

Hypothesis-driven experiment design following DeepMind's methodology//TPU-aware training infrastructure and Pathways architecture reasoning//Scaling law application to benchmark selection and result interpretation

Practice Library

Google exercises

Ready to ace your Google Research MTS interview?

Prepare for your Google Research MTS interview

ML research methodology and large-scale training system design cases calibrated to Google Research and DeepMind’s research-scientist, publish-or-perish MTS culture.

Socratify AI

The Interview

What Google is looking for

Research Interview

ML Research Methodology

01Hypothesis-Driven Experiment Design

02Ablation Study Structure and Scope

03Benchmark Selection and Validity

04Scientific Communication and Reproducibility

Systems Interview

Large-Scale ML Systems

01TPU/GPU Training Infrastructure

02Distributed Training Architecture (Pathways)

03Mixture-of-Experts System Design

04Long-Context Attention Optimization

Behavioral Interview

01Research Impact and Prioritization

02Intellectual Curiosity and Depth

03Collaborative Research Culture

04Long-Term Scientific Vision

How It Works

Master Google Research MTS Interviews

ML research methodology and large-scale training system design cases, scored on Google Research’s MTS evaluation dimensions. · Click to expand

Research Methodology

Practitioner

Training System Design

Practitioner

1Your Member of Technical Staff Goals

2Member of Technical Staff Case Prep

Design a research experiment to test whether chain-of-thought prompting improves...

LEARNHard

AI Research · Research MethodologyGoogle

3Frameworks

Framework

Is the training data mixture causing the reasoning capability gap?

Is STEM and reasoning data underrepresented in the training mix?

Current mix: 60% web crawl, 5% STEM corpora — MMLU requires ≥20% reasoning-heavy data

STEM token count: 3B vs GPT-3.5 estimated equivalent 15B — 5× reasoning deficit

Is benchmark contamination inflating GPT-3.5's reported MMLU score?

n-gram dedup check found 2.1% overlap between MMLU test questions and our training corpus

Our model trained without decontamination — actual capability gap may be narrower than 12pp

Were training hyperparameters suboptimal for this model and compute scale?

Was the learning rate schedule misconfigured for 7B scale?

Final LR: 1e-5 — Chinchilla-optimal for 7B at this token count is 3e-5 (3× too conservative)

12% of training steps ran at >2× optimal LR due to warmup misconfiguration — gradient instability window

Was the batch size appropriately scaled with model depth and sequence length?

Batch size: 2M tokens per step — compute-optimal for 7B scale is 4M (2× under-batch)

Gradient norm spiked >1.5 in final 20% of training — correlates with batch-size mismatch

Is the evaluation methodology creating a misleading comparison?

Are we using the same eval format (few-shot vs zero-shot) as the GPT-3.5 benchmark?

Our eval: 0-shot MMLU — GPT-3.5 official results were 5-shot (+8pp average on MMLU)

Switching to 5-shot narrows gap to 4pp — 67% of apparent gap is eval format mismatch

Is the remaining gap an instruction tuning gap rather than a pretraining gap?

GPT-3.5 is instruction-tuned via RLHF; our model is base pretrain — SFT typically adds 3-5pp on MMLU

Preliminary SFT on 100K instruction examples closes residual gap to <1pp — training deficit ruled out

Ablation Study Design

Distinctive

Scaling Law Reasoning

Strong

Evaluation Methodology

Meeting Bar

Training Diagnostics

Strong

4Feedback

How It Works

Master Google Research MTS Interviews

ML research methodology and large-scale training system design cases, scored on Google Research’s MTS evaluation dimensions. · Click to expand

1Your Member of Technical Staff Goals

Research Methodology

Practitioner

Training System Design

Practitioner

2Member of Technical Staff Case Prep

Design a research experiment to test whether chain-of-thought prompting improves...

LEARNHard

Google