OpenAI
Leading AI research laboratory developing state-of-the-art foundation models like GPT-4.
5 Rounds
~21 Days
Very Hard
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
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you had to pivot a major technical project because your initial approach fundamentally failed.
#Adaptability
#Problem Solving
#Resilience
Machine Learning Engineer
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Behavioral
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medium
OpenAI moves at an incredibly fast pace. Describe a situation where you had to ship a complex model or system under extreme time pressure with incomplete information.
#Time Management
#Prioritization
#Execution
Machine Learning Engineer
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Behavioral
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medium
How do you balance AI safety and alignment with model performance and capabilities in your day-to-day engineering decisions?
#AI Safety
#Ethics
#Decision Making
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you had to debug a deeply complex, distributed system issue or a silent failure in a machine learning model. How did you isolate the root cause?
#Debugging
#Problem Solving
#Resilience
Machine Learning Engineer
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Behavioral
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medium
OpenAI is focused on building safe AGI. How do you balance the need for rapid iteration and shipping product features with rigorous safety and alignment concerns?
#AI Safety
#Product Management
#Ethics
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you strongly disagreed with a senior researcher or engineer on the architectural direction of a model or system. How was it resolved?
#Conflict Resolution
#Communication
#Teamwork
Machine Learning Engineer
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Behavioral
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medium
What is the most challenging performance bottleneck you've ever optimized in a machine learning system? What tools did you use, and what was the impact?
#Performance Optimization
#Profiling
#Impact
Machine Learning Engineer
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Behavioral
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easy
Describe a project where you had to learn a completely new subfield of ML or systems engineering on the fly to deliver a critical feature.
#Adaptability
#Learning
#Ambiguity
Machine Learning Engineer
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Coding
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hard
Implement Multi-Head Attention from scratch in PyTorch. Ensure it is batched and optimized for memory.
#PyTorch
#Transformers
#Linear Algebra
Machine Learning Engineer
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Coding
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medium
Write a Byte-Pair Encoding (BPE) tokenizer from scratch. Given a corpus of text and a target vocabulary size, implement the training and tokenization functions.
#String Manipulation
#Data Structures
#NLP
Machine Learning Engineer
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Coding
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hard
Implement an autoregressive generation loop with KV Caching. Assume a simplified transformer block is provided.
#Memory Management
#Transformers
#PyTorch
Machine Learning Engineer
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Coding
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hard
Implement a Ring All-Reduce algorithm simulation. Given an array of N nodes, each with an array of numbers, write code to perform the scatter-reduce and all-gather phases.
#Networking
#Parallel Computing
#Algorithms
Machine Learning Engineer
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Coding
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medium
Implement a simplified Byte Pair Encoding (BPE) tokenizer. Given a corpus of text and a target vocabulary size, write a function to find the most frequent adjacent pair of characters or tokens and merge them.
#Strings
#Hash Maps
#NLP
Machine Learning Engineer
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Coding
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medium
Write a highly optimized self-attention mechanism in PyTorch from scratch. Include support for causal masking and explain the tensor shapes at each step.
#PyTorch
#Transformers
#Linear Algebra
Machine Learning Engineer
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Coding
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medium
Implement Top-K and Nucleus (Top-p) sampling given a tensor of logits. Ensure your implementation is numerically stable and efficient.
#Probability
#PyTorch
#Algorithms
Machine Learning Engineer
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Coding
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hard
Write a simple Autograd engine for scalar values from scratch. Implement the forward and backward passes for addition and multiplication.
#Calculus
#Graphs
#Object-Oriented Programming
Machine Learning Engineer
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Coding
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medium
Design a data structure for efficient KV cache eviction in an LLM serving engine. It must support O(1) inserts, O(1) lookups, and evict the least recently used sequence block.
#Data Structures
#Linked Lists
#Hash Maps
Machine Learning Engineer
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Coding
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hard
Write a function to perform matrix multiplication of two large 2D arrays. Optimize it for cache locality using block matrix multiplication (tiling).
#C++
#Performance Optimization
#Computer Architecture
Machine Learning Engineer
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Coding
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easy
Implement the Softmax function. Modify your implementation to ensure numerical stability when dealing with very large logits.
#Math
#Python
Machine Learning Engineer
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Coding
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medium
Implement Beam Search decoding for a language model given a function that returns the next-token probabilities.
#Search Algorithms
#Heuristics
#NLP
Machine Learning Engineer
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Coding
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medium
Implement a Token Bucket rate limiter for the OpenAI API. It needs to handle multiple users, support concurrent requests, and be highly performant.
#Concurrency
#System Design
#Data Structures
Machine Learning Engineer
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Coding
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hard
Write a PyTorch script to manually parallelize a simple feed-forward network across 2 GPUs using naive pipeline parallelism. Handle the forward and backward passes.
#PyTorch
#Distributed Computing
Machine Learning Engineer
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Coding
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medium
Given a Directed Acyclic Graph (DAG) representing a computation graph of ML operations, write an algorithm to schedule the operations on a fixed number of parallel workers to minimize total execution time.
#Graphs
#Scheduling
#Topological Sort
Machine Learning Engineer
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Coding
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hard
Implement a mock distributed parameter server. Write the worker code that computes gradients and the server code that aggregates them and updates weights, communicating via queues.
#Concurrency
#Distributed Systems
#Python
Machine Learning Engineer
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Coding
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hard
Implement the Aho-Corasick algorithm to efficiently search for a large dictionary of toxic words within a streaming text generation output.
#Trees
#Trie
#String Matching
Machine Learning Engineer
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Coding
•
medium
Given a list of text highlight spans (start_index, end_index) from multiple human labelers, write a function to merge all overlapping spans into a consolidated list of highlighted regions.
#Arrays
#Sorting
Machine Learning Engineer
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System Design
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hard
Design the inference architecture for a ChatGPT-like service to handle millions of concurrent users with minimal Time-To-First-Token (TTFT) and high throughput.
#Inference
#Scalability
#Concurrency
#Continuous Batching
Machine Learning Engineer
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System Design
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medium
Design a distributed data pipeline to ingest, filter, and deduplicate 10 Petabytes of raw web scrape data for LLM pre-training.
#Big Data
#MinHash
#Deduplication
#Distributed Computing
Machine Learning Engineer
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System Design
•
hard
Design the training infrastructure and orchestration system for a Reinforcement Learning from Human Feedback (RLHF) pipeline.
#RLHF
#PPO
#Architecture
#Orchestration
Machine Learning Engineer
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System Design
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hard
Design a fault-tolerant cluster orchestration system for training a 100B+ parameter model across 10,000 GPUs that can survive frequent node failures.
#Infrastructure
#Fault Tolerance
#Kubernetes
Machine Learning Engineer
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System Design
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hard
Design the serving infrastructure for ChatGPT to handle millions of concurrent users. How do you manage state, batching, and latency?
#Distributed Systems
#Inference Scaling
#Continuous Batching
Machine Learning Engineer
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System Design
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hard
How would you design a system to train a 100B+ parameter model across 10,000 GPUs? Detail the parallelism strategies you would use.
#Distributed Training
#3D Parallelism
#Network Topology
Machine Learning Engineer
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System Design
•
hard
Design a data pipeline to scrape, clean, deduplicate, and tokenize 10TB of raw web text data for LLM pretraining.
#Data Engineering
#MapReduce
#MinHash
Machine Learning Engineer
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System Design
•
hard
Design an end-to-end RLHF pipeline. Walk me through the system architecture from human labeling interfaces to the final PPO training loop.
#RLHF
#Data Pipelines
#Model Training
Machine Learning Engineer
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System Design
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medium
Design a system to detect and filter PII (Personally Identifiable Information) from a massive, continuously updating stream of training data.
#Security
#Stream Processing
#NLP
Machine Learning Engineer
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System Design
•
medium
Design an evaluation framework for the continuous deployment of new LLM checkpoints. How do you ensure a new model doesn't regress on coding tasks while improving on creative writing?
#MLOps
#Evaluation
#Testing
Machine Learning Engineer
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System Design
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hard
Design a multi-tenant vector database system to support embedding search for millions of users (e.g., for ChatGPT custom knowledge bases).
#Databases
#Information Retrieval
#Scalability
Machine Learning Engineer
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System Design
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hard
You are tasked with reducing the Time-To-First-Token (TTFT) and increasing the generation speed of an existing LLM API. Walk me through the specific optimizations you would implement.
#Inference Optimization
#Latency
#Hardware
Machine Learning Engineer
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Technical
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hard
During the distributed pre-training of a 70B parameter model, you observe sudden, unrecoverable loss spikes. Walk me through your step-by-step debugging process.
#Distributed Training
#Optimization
#Debugging
Machine Learning Engineer
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Technical
•
medium
Explain the mathematical intuition behind Rotary Position Embeddings (RoPE) and why it is preferred over absolute positional embeddings in modern LLMs.
#Mathematics
#Transformers
#Architecture
Machine Learning Engineer
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Technical
•
hard
Explain FlashAttention. How does it optimize memory bandwidth, and what are the trade-offs?
#CUDA
#Memory Bandwidth
#Hardware Optimization
Machine Learning Engineer
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Technical
•
medium
What are the specific trade-offs between Tensor Parallelism (TP), Pipeline Parallelism (PP), and Fully Sharded Data Parallelism (FSDP)? When would you use each?
#Model Parallelism
#GPU
#Networking
Machine Learning Engineer
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Technical
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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
Machine Learning Engineer
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Technical
•
hard
Explain how FlashAttention works. Why does it reduce memory bandwidth, and how does it achieve exact attention mathematically?
#Transformers
#CUDA
#Hardware Optimization
Machine Learning Engineer
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Technical
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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
Machine Learning Engineer
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Technical
•
hard
Explain Rotary Positional Embeddings (RoPE). Why are they preferred over absolute positional embeddings in modern LLMs?
#Transformers
#Mathematics
#NLP
Machine Learning Engineer
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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
Machine Learning Engineer
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Technical
•
medium
Explain the difference between Layer Normalization and RMSNorm. Why has the industry largely shifted to RMSNorm for LLMs?
#Deep Learning
#Optimization
Machine Learning Engineer
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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
Machine Learning Engineer
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Technical
•
easy
Explain the vanishing gradient problem. How do architectural innovations like Residual Connections (ResNets) and Transformers mitigate this issue?
#Deep Learning Basics
#Architecture
Difficulty Radar
Based on recent AI-sourced data.
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Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
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