Employer hiring guide

Anthropic

AI Lab · US · UK · GLOBAL

13candidate experiences

Candidate experiences

What candidates experienced

Based on 13 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 styleProduction-style implementation
6 of 12 experiences with assessment details
02
Assessment styleValues and safety judgement
6 of 12 experiences with assessment details
03
Assessment styleProject and technical deep dive
3 of 12 experiences with assessment details
04
Candidate preparationCompany mission and product
6 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.

10 of 12recruiter screen · 83%
6 of 12onsite loop · 50%
5 of 12coding · 42%
5 of 12technical screen · 42%
4 of 12behavioural · 33%
4 of 12hiring manager · 33%
3 of 12presentation · 25%
3 of 12system design · 25%

1–7 roundsReported among 11 experiences with round information.

About two to four weeks to About two to four weeksReported among 5 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

6 of 12production-style implementation · 50%
6 of 12values and safety judgement · 50%
4 of 12system-design trade-offs · 33%
3 of 12algorithmic problem-solving · 25%
3 of 12project and technical deep dive · 25%

Coding and online assessment

5 of 13general coding · 38%
4 of 13practical implementation · 31%

System design

3 of 11architecture trade-offs · 27%
3 of 11machine-learning systems · 27%
3 of 11system design · 27%

Behavioural and culture

7 of 13culture and values · 54%
5 of 13motivation and mission · 38%
5 of 13values and safety judgement · 38%
4 of 13communication under ambiguity · 31%
3 of 13collaboration and conflict · 23%

Projects and case studies

3 of 3presentations and project deep dives

Candidate preparation

What candidates recommend preparing

These patterns come only from experiences that included preparation detail.

6 of 12company mission and product · 50%
5 of 12practical implementation · 42%
3 of 12algorithms and data structures · 25%
3 of 12behavioural stories · 25%
3 of 12role-specific fundamentals · 25%

Communication and outcomes

How the process ended

Response

Heard-back and left-waiting findings use 10 experiences with a known response. Pending and unknown experiences are excluded.

Heard back10 of 10100%

Left waiting0 of 100%

Pending2 of 13Count among all included experiences

Unknown1 of 13Count among all included experiences

Blanked after interview0 of 100%

Reported outcomes

Raw counts among 12 experiences with a reported final outcome. These are not hiring probabilities.

2Offers

6Rejections

4Ongoing

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, system-design trade-offs, and values and safety judgement.

Reported route

Recruiter screen → Coding → System design → Behavioural → Hiring manager → Onsite loop

Assessment focus

Production-style implementation · System-design trade-offs · Values and safety judgement · Practical implementation · System design · Collaboration and conflict · Culture and values · Communication under ambiguity

How they prepared

Behavioural stories

Process shape

Full Loop · about 7 rounds · About two to four weeks · Recruiter Screen / System Design / Behavioural

Communication and outcome

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

The experience placed emphasis on production-style implementation, system-design trade-offs, and values and safety judgement. Preparation guidance focused on behavioural stories.

Candidate finding · Data and machine learning · Early Career · United States

One candidate experience reported assessment focused on incremental testing and debugging, practical work simulation, and values and safety judgement.

Reported route

Recruiter screen

Assessment focus

Incremental testing and debugging · Practical work simulation · Values and safety judgement · Debugging and code review · SQL and data · Machine-learning systems

How they prepared

Company mission and product · Explaining decisions and trade-offs

Process shape

Full Loop · about 6 rounds · About two to four weeks · Recruiter Screen

Communication and outcome

The employer replied. Outcome: Rejected

The experience placed emphasis on incremental testing and debugging, practical work simulation, and values and safety judgement. Preparation guidance focused on company mission and product and explaining decisions and trade-offs.

Candidate finding · Software engineering · Senior · UK and Ireland

One candidate experience reported assessment focused on production-style implementation.

Reported route

Recruiter screen

Assessment focus

Production-style implementation · General coding · Architecture trade-offs · Leadership and influence · Communication under ambiguity

How they prepared

Behavioural stories

Process shape

Partial Loop · about 2 rounds · About two to four weeks · Recruiter Screen

Communication and outcome

The employer replied. Outcome: Rejected

The experience placed emphasis on production-style implementation. Preparation guidance focused on behavioural stories.

Candidate finding · Software engineering · Staff Plus · United States

One candidate experience reported assessment focused on production-style implementation, system-design trade-offs, and project and technical deep dive.

Reported route

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

Assessment focus

Production-style implementation · System-design trade-offs · Project and technical deep dive · Values and safety judgement · APIs and data processing · Machine-learning systems · Collaboration and conflict · Motivation and mission · Culture and values · Presentations and project deep dives

How they prepared

Practical implementation

Process shape

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

Communication and outcome

The candidate reported receiving feedback. Outcome: Rejected

The experience placed emphasis on production-style implementation, system-design trade-offs, and project and technical deep dive. Preparation guidance focused on practical implementation.

Dataset context

What is represented in this view

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

13Candidate experiences

13Reached interview

7Full-loop experiences

2Role families represented

3Broad locations represented

2025–2026Interview years represented

Heard back among known responses10 of 10

Reported rounds · 11 with detail1–7

Commonly reported stagesrecruiter screen · onsite loop · coding

Supporting employer guidance

Compare what candidates experienced with what Anthropic 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 technical-interview guidance

allSoftware engineeringMultiple levelsChecked 2 Aug 2026

1
Application and review
Published stage
online
2
Remote technical interviews
Published stage
video
Anthropic says interviews use Google Meet and technical roles can use live-coding tools such as Colab and CodeSignal. The number and order of conversations are not published.
May assess: coding, experience, motivation, communication

Preparation and accessibility1 employer resource

What the employer publishes

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

Anthropic hiring guidanceAll roles · WEB

Reasonable adjustmentsA confidential reasonable-accommodation request route is published for any part of the application process.

Reapplication policyCandidates can reapply after 12 months, or sooner after a material change in experience or skills.

Working modelCurrent London engineering listings expect at least 25% office attendance; some roles may require more.

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 platformGreenhouse

Feedback policyAnthropic says it does not provide feedback on résumés or interviews.

PayNo connected benchmark

No pay benchmark for this location is connected.

What current job ads disclose12 dated adverts checked

12/12salary disclosed

12/12working model disclosed

0/12closing date disclosed

0/12process disclosed

View the 12 sampled job adverts

Full-Stack Software Engineer, Reinforcement LearningSan Francisco, CA · New York City, NY · checked 5 Aug 2026

View advert ↗

Research Engineer, Machine Learning (Reinforcement Learning)San Francisco, CA · New York City, NY · checked 5 Aug 2026

View advert ↗

Senior+ Software Engineer, Research ToolsSan Francisco, CA · New York City, NY · checked 5 Aug 2026

View advert ↗

Staff+ Software Engineer, BackendSan Francisco, CA · New York City, NY · Seattle, WA · checked 5 Aug 2026

View advert ↗

Research Engineer, Machine Learning (Reinforcement Learning)London · checked 5 Aug 2026

View advert ↗

Staff+ Software Engineer, Account CompromiseLondon · checked 5 Aug 2026

View advert ↗

Research Engineer, RL Scaling ScienceLondon · checked 5 Aug 2026

View advert ↗

Staff Software Engineer, AI Reliability EngineeringLondon · checked 5 Aug 2026

View advert ↗

Staff+ Software Engineer, Safeguards InfrastructureLondon · checked 5 Aug 2026

View advert ↗

Senior Staff+ Software Engineer, Kubernetes PlatformLondon · checked 5 Aug 2026

View advert ↗

Research Engineer, Machine Learning (RL Velocity)London · checked 5 Aug 2026

View advert ↗

Senior Software Engineer, InferenceLondon · checked 5 Aug 2026

View advert ↗

Employer links used13 employer links

Overall assessment style

Coding and online assessment

System design

Behavioural and culture

Projects and case studies

Response

Reported outcomes

Published technical-interview guidance

Application and review

Remote technical interviews