OpenAI

Leading AI research laboratory developing state-of-the-art foundation models like GPT-4.

5 Rounds ~21 Days Very Hard

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The Interview Loop

Recruiter Screen (30 min)

Standard fit check, behavioral questions, and resume overview.

Technical Loop (3-4 Rounds)

Deep dive into domain knowledge, coding, and system design.

Interview Question Bank

All Roles Machine Learning Engineer 8

All Topics Algorithms 12 System Design 10 ML Theory 8 Culture Fit 7 Deep Learning Implementation 4 Deep Learning 3 Distributed Systems 2 ML Engineering 2

Machine Learning Engineer • Technical • medium

Derive the exact GPU memory requirements for training a 7 Billion parameter model using the Adam optimizer in mixed precision (fp16/bf16).

#Hardware #Optimization #Memory Management

Practice

Machine Learning Engineer • Technical • hard

Explain how FlashAttention works. Why does it reduce memory bandwidth, and how does it achieve exact attention mathematically?

#Transformers #CUDA #Hardware Optimization

Practice

Machine Learning Engineer • Technical • hard

What are the mathematical and practical differences between Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO) in the context of RLHF?

#Reinforcement Learning #RLHF #Loss Functions

Practice

Machine Learning Engineer • Technical • hard

Explain Rotary Positional Embeddings (RoPE). Why are they preferred over absolute positional embeddings in modern LLMs?

#Transformers #Mathematics #NLP

Practice

Machine Learning Engineer • Technical • medium

What is the difference between Tensor Parallelism and Pipeline Parallelism? When would you use each, and what are their respective communication bottlenecks?

#Distributed Systems #Parallel Computing

Practice

Machine Learning Engineer • Technical • medium

Explain the difference between Layer Normalization and RMSNorm. Why has the industry largely shifted to RMSNorm for LLMs?

#Deep Learning #Optimization

Practice

Machine Learning Engineer • Technical • medium

How do you handle catastrophic forgetting when fine-tuning a pre-trained LLM on a highly specific, narrow domain?

#Fine-tuning #Transfer Learning

Practice

Machine Learning Engineer • Technical • easy

Explain the vanishing gradient problem. How do architectural innovations like Residual Connections (ResNets) and Transformers mitigate this issue?

#Deep Learning Basics #Architecture

Practice

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Based on recent AI-sourced data.

Meet Your Interviewers

The "Standard" Interviewer

Senior Engineer

Focuses on core competencies, system constraints, and clear communication.

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Unwritten Rules

Think Out Loud

Always explain your thought process before writing code or drawing architecture.

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