Employer hiring guide

OpenAI

AI Lab · US · UK · GLOBAL

15candidate experiences

Candidate experiences

What candidates experienced

Based on 15 candidate experiences.

Experiences vary by role, level, location and year. These findings describe the experiences in this dataset, not every applicant.

What stands out

Details a standard process page usually leaves out

Every pattern shows how many relevant experiences support it and how many contained the detail needed to assess it.

01
Assessment styleSystem-design trade-offs
7 of 15 experiences with assessment details
02
Assessment styleOperational reliability
5 of 15 experiences with assessment details
03
Assessment styleAI-assisted work
4 of 15 experiences with assessment details
04
Candidate preparationPractical implementation
8 of 12 experiences with preparation details

Common process

How candidates moved through the process

These are stages and ranges reported by candidates, not a fixed company-wide interview route.

9 of 14coding · 64%
7 of 14recruiter screen · 50%
6 of 14system design · 43%
5 of 14hiring manager · 36%
4 of 14presentation · 29%
3 of 14onsite loop · 21%
3 of 14technical screen · 21%

1–8 roundsReported among 13 experiences with round information.

Up to about two weeks to About one to two monthsReported among 8 experiences containing timeline information.

Assessment themes

What the interviews actually focused on

Each line uses only experiences containing relevant assessment detail, so missing information does not dilute the denominator.

Overall assessment style

7 of 15system-design trade-offs · 47%
5 of 15algorithmic problem-solving · 33%
5 of 15operational reliability · 33%
5 of 15project and technical deep dive · 33%
4 of 15AI-assisted work · 27%
4 of 15incremental testing and debugging · 27%
4 of 15ML and model internals · 27%
3 of 15production-style implementation · 20%

Coding and online assessment

4 of 15algorithms and data structures · 27%
4 of 15general coding · 27%
3 of 15AI-assisted coding · 20%
3 of 15debugging and code review · 20%
3 of 15practical implementation · 20%

System design

5 of 14reliability and scale · 36%
4 of 14API and service design · 29%
4 of 14system design · 29%
3 of 14distributed systems · 21%

Behavioural and culture

7 of 15behavioural discussion · 47%
5 of 15motivation and mission · 33%
5 of 15ownership and delivery · 33%
4 of 15technical project discussion · 27%
3 of 15collaboration and conflict · 20%

Projects and case studies

4 of 7presentations and project deep dives · 57%

Candidate preparation

What candidates recommend preparing

These patterns come only from experiences that included preparation detail.

8 of 12practical implementation · 67%
7 of 12company mission and product · 58%
4 of 12system-design trade-offs · 33%

Communication and outcomes

How the process ended

Response

Heard-back and left-waiting findings use 12 experiences with a known response. Pending and unknown experiences are excluded.

Heard back12 of 12100%

Left waiting0 of 120%

Pending2 of 15Count among all included experiences

Unknown1 of 15Count among all included experiences

Blanked after interview0 of 120%

Reported outcomes

Raw counts among 14 experiences with a reported final outcome. These are not hiring probabilities.

2Offers

10Rejections

2Ongoing

0Withdrew

0Declined or altered offers

Candidate findings

What individual experiences reveal

Each row describes one candidate experience and keeps its process, assessment and outcome context together. It is not a company-wide claim.

Candidate finding · Software engineering · Senior · United States

One candidate experience reported assessment focused on production-style implementation, existing-codebase work, and algorithmic problem-solving.

Reported route

Recruiter screen → Technical screen → Presentation → Onsite loop

Assessment focus

Production-style implementation · Existing-codebase work · Algorithmic problem-solving · End-to-end product thinking · Project and technical deep dive · Practical implementation · Working in an existing codebase · Debugging and code review · API and service design · Collaboration and conflict · Leadership and influence · Ownership and delivery · Communication under ambiguity · Technical project discussion · Presentations and project deep dives

How they prepared

Practical implementation · Company mission and product

Process shape

Full Loop · about 6 rounds · About two to four weeks · Recruiter Screen / Case Or Presentation

Communication and outcome

The employer replied. Outcome: Rejected

The experience placed emphasis on production-style implementation, existing-codebase work, and algorithmic problem-solving. Preparation guidance focused on practical implementation and company mission and product.

Candidate finding · Data and machine learning · Senior · United States

One candidate experience reported assessment focused on incremental testing and debugging, AI-assisted work, and project and technical deep dive.

Reported route

Recruiter screen → Coding → Hiring manager → Take-home assessment

Assessment focus

Incremental testing and debugging · AI-assisted work · Project and technical deep dive · Data analysis and experimentation · Practical work simulation · Debugging and code review · AI-assisted coding · SQL and data · System design · Leadership and influence · Motivation and mission · Ownership and delivery · Technical project discussion · Take-home work

How they prepared

Company mission and product

Process shape

Full Loop · about 8 rounds · About two to four weeks · Recruiter Screen / Take Home

Communication and outcome

The employer replied. Outcome: Rejected

The experience placed emphasis on incremental testing and debugging, AI-assisted work, and project and technical deep dive. Preparation guidance focused on company mission and product.

Candidate finding · Software engineering · United States · 2026

One candidate experience reported assessment focused on concurrency, ML and model internals, and operational reliability.

Reported route

Recruiter screen → Coding → System design → Behavioural → Presentation → Onsite loop

Assessment focus

Concurrency · ML and model internals · Operational reliability · System-design trade-offs · Project and technical deep dive · Practical implementation · Model and numerical implementation · Distributed systems · Reliability and scale · Collaboration and conflict · Motivation and mission · Ownership and delivery · Technical project discussion · Presentations and project deep dives

How they prepared

Practical implementation · System-design trade-offs · Project examples · Company mission and product

Process shape

Full Loop · about 7 rounds · About one to two months · Recruiter Screen / System Design / Behavioural / Case Or Presentation

Communication and outcome

The experience was still awaiting a final outcome. Outcome: Ongoing

The experience placed emphasis on concurrency, ML and model internals, and operational reliability. Preparation guidance focused on practical implementation and system-design trade-offs.

Candidate finding · Software engineering · Early Career · United States

One candidate experience reported assessment focused on values and safety judgement.

Reported route

Offer stage

Assessment focus

Values and safety judgement · Online assessment · System design · Motivation and mission

How they prepared

Company mission and product · Role-specific fundamentals

Process shape

Full Loop · Timing was reported, but not in a consistent range · Online Assessment

Communication and outcome

The employer replied. Outcome: Offered

The experience placed emphasis on values and safety judgement. Preparation guidance focused on company mission and product and role-specific fundamentals.

Dataset context

What is represented in this view

Coverage changes with the filters above, so the context stays attached to every finding.

15Candidate experiences

15Reached interview

8Full-loop experiences

4Role families represented

1Broad locations represented

2024–2026Interview years represented

Heard back among known responses12 of 12

Reported rounds · 13 with detail1–8

Commonly reported stagescoding · recruiter screen · system design

Supporting employer guidance

Compare what candidates experienced with what OpenAI publishes

Candidate experiences are the main guide. Employer pages can corroborate or add context, but routes still vary by role, level and location. Last checked 2 Aug 2026.

Process: reported and published1 published route to compare

What the employer publishes

Treat this as a documented guide, then compare it with the candidate-reported stages and ranges above.

Selected published route

Published software-engineering path

allSoftware engineeringMultiple levelsChecked 2 Aug 2026

1
Application and résumé review
Published stage
online
OpenAI says résumé review typically takes one week.
2
Introductory call
Published stage
video
May assess: experience, motivation, goals
3
Skills-based assessment
Role-dependent
mixed
Role-dependent formats can include pair coding, a take-home project or technical tests, and more than one assessment may be used.
May assess: solution design, code quality, performance, testing
4
Final interviews
Published stage
video
Typically four to six hours with four to six people over one or two days.
May assess: technical expertise, communication, collaboration
5
Decision and possible references
Role-dependent
Format varies by role
OpenAI says candidates should hear within one week of final interviews; references may be requested.

Published total timelineFinal interviews typically take 4–6 hours over 1–2 days; a decision is targeted within one week.

Preparation and accessibility1 employer resource

What the employer publishes

Official preparation resources and support details can add role-specific context to candidate recommendations.

OpenAI interview guideAll roles · WEB

Reasonable adjustmentsApplicants can request reasonable accommodations through the route linked from current job listings.

Communication and applications2 published details

What the employer publishes

Published tracking and feedback policies describe intended support; candidate experiences show how communication was experienced.

Application platformAshby

Feedback policyOpenAI publishes one-week targets for résumé review, post-assessment progression and the final decision; actual timing can vary by role.

PayBroad pay benchmark available

Levels.fyi benchmark available

This company-level page is not tied to one vacancy or level, so a broad figure is not presented as a role salary. Use a live-role comparison for a closer match.

View pay data ↗

What current job ads disclose9 dated adverts checked

5/9salary disclosed

0/9working model disclosed

0/9closing date disclosed

0/9process disclosed

View the 9 sampled job adverts

Forward Deployed Software Engineer — NYCNew York City, NY · checked 5 Aug 2026

View advert ↗

Full Stack Software Engineer, API ExperienceNew York City, NY · checked 5 Aug 2026

View advert ↗

Software Engineer, Compute — StorageSan Francisco, CA · New York City, NY · checked 5 Aug 2026

View advert ↗

Software Engineer, Integrity FoundationsSan Francisco, CA · checked 5 Aug 2026

View advert ↗

Software Engineer, Cloud InfrastructureLondon · checked 5 Aug 2026

View advert ↗

Software Engineer, ChatGPT InfrastructureLondon · checked 5 Aug 2026

View advert ↗

Software Engineer, Codex EnterpriseLondon · checked 5 Aug 2026

View advert ↗

Software Engineer, GPU Infrastructure, ChatGPT EngineeringLondon · checked 5 Aug 2026

View advert ↗

Software Engineer, Platform SystemsLondon · checked 5 Aug 2026

View advert ↗

Employer links used11 employer links

Overall assessment style

Coding and online assessment

System design

Behavioural and culture

Projects and case studies

Response

Reported outcomes

Published software-engineering path

Application and résumé review

Introductory call

Skills-based assessment

Final interviews

Decision and possible references