Prepare for your Anthropic MTS interview

AI safety research methodology and alignment system design cases calibrated to Anthropic’s mission-driven, interpretability-focused MTS culture.

Socratify AI

The Interview

What Anthropic is looking for

Research Design Interview

Alignment Research Design

01Constitutional AI System Reasoning

02RLHF Pipeline Architecture

03Interpretability Experiment Design

04Safety-Capability Trade-off Analysis

ML Systems Interview

Training Infrastructure Design

01Evaluation Framework & Contamination Control

02Red-Teaming Pipeline Architecture

03Scalable Oversight Methodology

04Model Behavior Monitoring Design

Behavioral Interview

01Long-Horizon Safety Reasoning

02Intellectual Rigor & Epistemic Honesty

03Collaboration Under Deep Uncertainty

04Mission Alignment & Urgency

Constitutional AI and RLHF pipeline design//Evaluation contamination detection and benchmark validity//Safety-capability tradeoff reasoning under uncertainty

Practice Library

Anthropic exercises

Ready to ace your Anthropic MTS interview?

Prepare for your Anthropic MTS interview

AI safety research methodology and alignment system design cases calibrated to Anthropic’s mission-driven, interpretability-focused MTS culture.

Socratify AI

The Interview

What Anthropic is looking for

Research Design Interview

Alignment Research Design

01Constitutional AI System Reasoning

02RLHF Pipeline Architecture

03Interpretability Experiment Design

04Safety-Capability Trade-off Analysis

ML Systems Interview

Training Infrastructure Design

01Evaluation Framework & Contamination Control

02Red-Teaming Pipeline Architecture

03Scalable Oversight Methodology

04Model Behavior Monitoring Design

Behavioral Interview

01Long-Horizon Safety Reasoning

02Intellectual Rigor & Epistemic Honesty

03Collaboration Under Deep Uncertainty

04Mission Alignment & Urgency

Constitutional AI and RLHF pipeline design//Evaluation contamination detection and benchmark validity//Safety-capability tradeoff reasoning under uncertainty

How It Works

Master Anthropic MTS Interviews

AI safety research methodology and alignment system design cases, scored on Anthropic’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 an evaluation framework to distinguish genuine capability improvements from...

LEARNHard

AI Safety · Evaluation DesignAnthropic

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 Anthropic MTS Interviews

AI safety research methodology and alignment system design cases, scored on Anthropic’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 an evaluation framework to distinguish genuine capability improvements from...

LEARNHard

Anthropic