Prepare for your OpenAI MTS interview

Research methodology and large-scale training system design cases calibrated to OpenAI’s safety-conscious, research-first MTS culture.

Socratify AI

The Interview

What OpenAI is looking for

Research Design Interview

Training System Design

01Compute-Optimal Run Planning

02Training Stability & Hyperparameter Judgment

03Evaluation Framework Design

04Data Quality & Decontamination

ML Theory Interview

ML Theory & Diagnostics

01Scaling Law Reasoning

02Ablation Study Design

03Loss Landscape Analysis

04Emergent Capability Prediction

Behavioral Interview

01Safety-First Decision Making

02Research Rigor Under Ambiguity

03Cross-Functional Research Impact

04Mission & Long-Term Alignment

Safety-first training regime design and evaluation//Compute-optimal run planning using scaling law reasoning//Evaluation framework design against contamination artifacts

Practice Library

OpenAI exercises

Ready to ace your OpenAI MTS interview?

Prepare for your OpenAI MTS interview

Research methodology and large-scale training system design cases calibrated to OpenAI’s safety-conscious, research-first MTS culture.

Socratify AI

The Interview

What OpenAI is looking for

Research Design Interview

Training System Design

01Compute-Optimal Run Planning

02Training Stability & Hyperparameter Judgment

03Evaluation Framework Design

04Data Quality & Decontamination

ML Theory Interview

ML Theory & Diagnostics

01Scaling Law Reasoning

02Ablation Study Design

03Loss Landscape Analysis

04Emergent Capability Prediction

Behavioral Interview

01Safety-First Decision Making

02Research Rigor Under Ambiguity

03Cross-Functional Research Impact

04Mission & Long-Term Alignment

Safety-first training regime design and evaluation//Compute-optimal run planning using scaling law reasoning//Evaluation framework design against contamination artifacts

How It Works

Master OpenAI MTS Interviews

Training system design and ML research methodology cases, scored on OpenAI’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

Your 7B model underperforms GPT-3.5 on MMLU by 12pp despite 3× the compute. Diagnose the...

LEARNHard

AI Research · ML DiagnosticsOpenAI

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 OpenAI MTS Interviews

Training system design and ML research methodology cases, scored on OpenAI’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

Your 7B model underperforms GPT-3.5 on MMLU by 12pp despite 3× the compute. Diagnose the...

LEARNHard

OpenAI