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Hari KharelStudy

OpenAI

Work-like, multi-part coding in CoderPad, against the clock, with follow-ups that change the requirements partway through. Algorithms show up inside practical tasks: versioned key-value stores, iterators, credit ledgers. The final loop adds scale-focused system design, a project deep-dive with relentless follow-ups, and a “why OpenAI” thread that runs through every round.

The loop

Section titled “The loop”
Stage Format Evidence
Resume review About 1 week official
Recruiter / HM screen About 30 min, sometimes with early behavioral questions reported
Skills assessment Pair coding, take-home, or technical test; some roles have more than one. Typically a 1-hour coding screen, sometimes a separate design screen official
Final interviews “4-6 hours with 4-6 people over 1-2 days”, usually virtual: 1-2 coding, system design, project deep-dive (about 30 min presentation plus Q&A), 1-2 behavioral official
Team match / levelling After the loop. Down-levelling is reported thin
  • Tools. Live coding in CoderPad. An OpenAI recruiter (2025): “it’s not like coding in a full IDE where you have autocomplete in a debugger.” official Take-homes run in your own setup.
  • AI. Expectations “vary by interview”: some formats intentionally allow AI tools, others test independent problem solving, and the prep materials say which applies. official If AI is allowed on a take-home, you must still explain and modify every line live. reported Assume no AI in CoderPad rounds unless told otherwise.
  • Language. Usually your choice; Python is the safe default. reported
  • Pace. OpenAI aims to update you about a week after each stage. Total time is 3-8 weeks.

What the coding rounds test

Section titled “What the coding rounds test”

Style. “Multi-part coding problem under 1 hour with tests passing.” Follow-ups often arrive mid-solution. Some loops add refactoring, debugging or OOP rounds. reported

Conflict in the evidence. Most reports say it’s not LeetCode-heavy, yet the recruiter advice is to “brush up on data structures and algorithms”. official Read both as one message: classic algorithms appear, embedded in a practical task.

Problem family candidates report How well attested Study first
Key-value store variants: time-versioned “as of” reads, serialize/restore, cloud state storage Most frequent family reported Versioning, Binary search, Sorted containers
In-memory database with SQL-like operations reported Parsing, Indexing, Class design
Resumable / stateful iterator reported Iterator design, State machines
Credit / token balance tracking (GPU credits) with expiry reported Ledgers, Heaps, Time handling
Concurrent web crawler; fix a race condition thin Crawling, Concurrency
Spreadsheet with formulas and dependency updates thin Topological sort, Expression evaluation
Grid spread simulation; follow graph with snapshots thin BFS, Snapshots
LRU / LFU cache; path normalisation; IP-address handling thin each Caching, File paths

Stated bar: “well-designed solutions to the challenge, high-quality code, optimal performance” and good test coverage. official Interviewers care about “how you approached it” and “the obstacles along the way”, so think aloud. official

System design

Section titled “System design”

Scale-focused (plan for 100x-1000x load). You’re expected to drive the session and walk one request through the system end to end. reported Reported prompts:

  • CI/CD in the style of GitHub Actions, or a cloud dev box
  • Slack-style chat
  • the ChatGPT / Playground backend
  • a payment system; webhook delivery
  • online chess; video streaming; a cloud IDE

Read Scheduling, Event-driven design, Idempotency, Load balancing and Distributed thinking.

Deep-dive and behavioral

Section titled “Deep-dive and behavioral”
  • Project deep-dive. Your own contribution, the alternatives you rejected, what you’d change. Expect follow-ups until you reach the edge of what you know. Shallow stories are a named failure mode. reported
  • Behavioral. “Why OpenAI” comes up in nearly every round. Also expect disagreement, failure, ambiguity and cross-team work, and sometimes AI safety (when should a model ship?). reported OpenAI says it looks for collaboration, communication, openness to feedback and mission alignment, and recruiters suggest STAR answers of 2-3 minutes, backed by data. official Read the OpenAI Charter.

Why people fail

Section titled “Why people fail”
  1. Not finishing the multi-part problem: too long on part 1, no time for the follow-ups.
  2. Project stories that collapse under questioning: “we” without “I”, no numbers, no alternatives considered.
  3. A weak mission tie-in: a generic “why OpenAI”.
  4. The bar: one aggregator counts 1 offer in 26 reported US SWE loops (about 4%; small sample). thin

Your plan

Section titled “Your plan”

Use the Sandbox’s OpenAI filter. These map most closely to the reported families. Practise in a plain editor with no autocomplete, and give yourself 60 minutes per problem. When Level 1 passes, treat each new level as the interviewer’s follow-up.

2 weeks 4 weeks
Week 1 Time-Versioned Key-Value Store, Resumable Iterator, GPU Credit Ledger, Mini SQL Engine (Levels 1-2). Write two project stories The 2-week Week 1, plus Versioned Record Store and LRU Cache
Week 2 Spreadsheet, Web Crawler, Follow Graph with Snapshots, then one timed mock with Lease-Based Task Queue. Two design sessions: CI/CD system, chat. Rehearse “why OpenAI” Mini SQL Engine to the end, Spreadsheet, Path Resolver, Length-Prefixed String Codec
Week 3 Web Crawler, Follow Graph with Snapshots, Outbreak Simulator, Memory Allocator. Three design sessions: CI/CD, ChatGPT backend, webhooks
Week 4 Two full timed mocks (Lease-Based Task Queue, Mini Redis). A deep-dive mock: a friend asks “why?” five times on every decision

Ask your recruiter: Is any round AI-allowed, and with which tool? CoderPad or my own IDE? Will the code be run against tests? Is there a separate design screen?

Sources

Section titled “Sources”