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An Anthropic hiring manager whose teams have led Claude Cowork and Claude Desktop said they stopped asking software engineering candidates coding questions. The reported reason is that data-structures-and-algorithms interviews provided no useful signal for that team; the source does not establish that Anthropic has changed hiring across the company.
A hiring manager at Anthropic said their team has stopped asking candidates for software engineering jobs to solve coding questions, saying data-structures-and-algorithms interviews gave the team no useful hiring signal. The report concerns the manager’s team, which has led work on Claude Cowork and Claude Desktop; it does not say the company has removed coding interviews from every engineering hiring process.
The development was reported in The Pragmatic Engineer newsletter, The Pulse, which cited a blog post by Felix Rieseberg, an Anthropic hiring manager. Rieseberg’s teams have led Claude Cowork and Claude Desktop. According to the report, the team stopped asking coding questions when interviewing candidates for software engineering roles.
The stated reason was that the team got “zero signal” from data-structures-and-algorithms interviews. That is a judgment about the usefulness of one interview method for this team, not evidence that coding ability is irrelevant to engineering work or that Anthropic has ended all technical assessment. The source does not describe what assessments replaced the questions.
The Pulse framed a wider change as possible, saying the rest of Anthropic and other AI-native startups could follow. That is a forecast, not a confirmed company-wide policy or a reported decision by other employers. No figures were provided on how many candidates or roles are covered by the team’s approach.
How Anthropic Teams Assess Engineers
The report matters to software engineers and job seekers because it challenges the use of algorithm-focused coding interviews as a standard way to screen candidates. If an employer finds that such exercises do not help distinguish candidates who can perform the work, dropping them could change how applicants prepare and what hiring teams evaluate.
For employers, the decision raises a practical question: what evidence should replace a test that a team considers uninformative? The source does not specify an alternative, so it is not possible to say whether the team relies more on project discussions, work samples, experience, or other methods. Any claim that this approach produces better hiring outcomes would go beyond the reported information.
The reference to Anthropic also gives the development attention beyond one hiring team. The company makes Claude, a prominent set of AI products, and the newsletter suggests other AI-focused firms may reconsider their processes. But the report identifies no other company that has adopted the same change, and it supplies no evidence of a broader industry shift.
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A Team-Level Hiring Change
The report appears in The Pulse, a newsletter series from The Pragmatic Engineer covering developments in technology companies and startups. Its item about Anthropic centers on Felix Rieseberg’s account of his team’s recruitment process, rather than an announcement from Anthropic’s corporate communications or a company-wide hiring policy document.
The distinction matters: a hiring manager can describe how their own team interviews candidates, but that alone does not establish the rules used by other teams at the same employer. The source identifies Rieseberg’s teams with Claude Cowork and Claude Desktop, yet gives no information about how many engineers they hire, when the interview change took effect, or whether Anthropic’s other engineering groups use similar methods.
Scope and Replacement Assessments
The available report does not establish whether Anthropic has changed its company-wide interview policy, whether other teams have followed, or whether the decision applies to every software engineering role on Rieseberg’s teams. It also does not give a date for the change or explain how long the team evaluated the interview format before dropping it.
It remains unclear what the team uses instead to assess candidates’ engineering skills and how it measures whether the revised process works. The “zero signal” description is attributed to the hiring manager’s team; the source provides no study, candidate data, or comparison of hiring outcomes. The suggestion that other AI-native startups may follow is speculation in the newsletter, not a confirmed development.
Whether Other Teams Follow
The next meaningful developments would be confirmation from Anthropic about whether this is limited to one team or reflects a broader recruiting policy, along with detail about what assessments candidates now face. The source does not announce a company response, a rollout schedule, or a formal evaluation of the change.
For now, the report supports a narrower conclusion: one Anthropic hiring manager’s team has stopped asking coding questions because its members found the format unhelpful. Whether other teams or AI-focused employers adopt similar practices remains to be seen.
Key Questions
Did Anthropic eliminate coding interviews across the company?
The report does not say that. It describes a change by a team led by hiring manager Felix Rieseberg; no company-wide policy change is confirmed.
Why did the team stop asking coding questions?
According to the report, the team found that data-structures-and-algorithms interviews gave it “zero signal” when assessing candidates. The source does not provide supporting data or further detail on how the team reached that conclusion.
What replaces coding questions in the interviews?
The report does not specify what assessment methods the team uses instead. It would be inaccurate to claim that a particular alternative has been adopted.
Have other AI companies dropped coding interviews too?
The newsletter says other AI-native startups could follow, but it does not identify any that have made the same change. That possibility remains a forecast, not a confirmed trend.
Source: newsletter.pragmaticengineer.com
