OpenAI Software Engineer candidates report a recruiter call, coding and system design screens, and further coding, design and behavioral rounds, often with a hiring-manager conversation. Coding is often practical and design leans toward scale.
$230K
Avg. Base Comp
$850K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe OpenAI's Software Engineer loop as practical, with less emphasis on recognizing puzzles than on building working code. One candidate was asked to build an in-memory database in a language of their choice, another to create a database ORM step by step, and another to implement a chat feature in an existing application. Some reports include requirements that change mid-round: in one rolling event aggregator problem, the interviewer pushed on out-of-order events that broke the candidate's first design.
Classic algorithm material still appears. One candidate got an Alien Dictionary-style graph problem built on topological sorting, and another described a coding screen as essentially a LeetCode medium.
Design questions lean toward scale. Reports mention enforcing usage limits in a chat product, a webhook delivery platform with retries and per-tenant rate limiting, and designing ChatGPT with the interviewer focused on scheduling, GPU allocation and autoscaling. One candidate also had a 48-hour take-home to build an in-process queue, where clean code and trade-offs mattered more than feature count. Hiring-manager and behavioral conversations cover past projects, debugging style, collaboration and scaling trade-offs.
Outcomes are mixed, with more rejections than offers. Practice building small systems from scratch, explain your trade-offs out loud as you go, and expect new constraints to appear before you finish.
Synthesized from 48 candidate reports by our editorial team.
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Topics based on recent interview experiences.
| Question | |
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| Google Maps Improvement | |
| Resumable Fact Table Load | |
| Hurdles In Data Projects | |
| LLM Enterprise Search | |
| Spanish Scrabble | |
| Transformer Encoder Layer | |
| Messenger Service Design | |
| Unlimited Plan Abuse | |
| Data Pipelines and Aggregation | |
| Moving Window | |
| Cloud-Agnostic Deployments | |
| Swipe Payment API | |
| Scalable Data Pipelines | |
| LRU Cache 1 | |
| Programming Risk Combat | |
| Statistically Significant Test | |
| Weighted Average With Missing Dates | |
| Trial Test Analysis | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Empty Neighborhoods | |
| Closest SAT Scores | |
| Top 5 Turnover Risk | |
| Monthly Customer Report | |
| Largest Salary by Department | |
| Top 3 Users | |
| Random SQL Sample |
Synthesized from candidate reports. Individual experiences may vary.
Candidates describe an opening recruiter conversation, about 30 minutes in one report. Topics included background, current tech stack, why you want OpenAI and, in one case, openness to relocating to San Francisco.
Screens combine coding and system design. One candidate had a coding and a design interview split by a one-hour break. Another reported a 90-minute round mixing coding and a mini system design. A third had a code screen with two interviewers, one on coding and one on design-style questions. Problems were often practical rather than pure algorithm drills.
Onsite structure varies by report. One candidate described an onsite of about four hours. Another reported three coding rounds and one cross-functional round in total. A third had a 48-hour take-home plus further coding and system design rounds, and another loop included an iOS code session and a final behavioral interview. Expect follow-up constraints and questions about the trade-offs behind each decision.
A hiring-manager conversation appears in several reports. One candidate found it conversational, with discussion of team growth and user demand. Another said it focused on past projects, debugging style and scaling trade-offs. A third described a short, roughly 20-minute behavioral call about a recent challenge.
