Anthropic's Hiring Test for Performance Engineers: A Case Study in AI Capability Outpacing Human Design

The challenge of designing a hiring test that can reliably assess a candidate's engineering skill is a perennial problem for tech companies. The goal is to create a task that is difficult enough to filter for top talent but fair enough to give all candidates a chance. For Anthropic, the company behind the Claude AI models, this challenge took on a new dimension: their own AI kept beating the test.

In a recent blog post, Anthropic detailed how it had to redesign its take-home assessment for performance engineering roles after discovering that Claude could consistently solve the problems the test was designed to evaluate. The original test, which the company has now released publicly, was intended to assess a candidate's ability to diagnose and optimize performance issues in a distributed system. It presented a scenario involving a simulated service with high latency and tasked the candidate with identifying bottlenecks and proposing improvements.

The problem, Anthropic explains, was that the test was effectively a "closed-book" exercise in reasoning about system behavior—a domain where large language models like Claude excel. When Anthropic's team ran the original test through Claude, the model not only identified the obvious performance issues but also proposed nuanced optimizations that would be expected from a seasoned engineer. This forced the company to confront a critical question: if an AI can pass a test designed for human experts, what does that test actually measure?

This incident highlights a growing tension in technical hiring. As AI models become more proficient at tasks that were once the exclusive domain of skilled professionals—from writing code to diagnosing system failures—the benchmarks we use to evaluate human talent are becoming obsolete. Anthropic's solution was to redesign the test to focus on areas where human judgment and creativity still hold a clear advantage. The new version of the assessment places greater emphasis on open-ended problem-solving, trade-off analysis, and the ability to communicate complex technical decisions—skills that are harder for AI to replicate convincingly.

The release of the original test is more than a curiosity; it's a practical resource for other engineering teams. By examining the problems that Claude could solve, hiring managers can better understand the current capabilities of AI and adjust their own evaluation methods accordingly. The test itself is a well-constructed exercise in performance engineering, involving a simulated microservice architecture with deliberate bottlenecks. Candidates are asked to profile the system, identify the root causes of latency, and propose both short-term fixes and long-term architectural improvements.

For those interested in seeing the test firsthand, Anthropic has made the full problem statement and reference materials available on their engineering blog. The company also provides a detailed breakdown of how Claude approached the problem, including the specific optimizations it suggested. This transparency is valuable for the broader tech community, as it provides a concrete example of how AI capabilities are evolving and what that means for human-centric workflows.

The implications extend beyond hiring. If an AI can diagnose performance issues in a complex system, it can also be used as a tool to augment human engineers. Anthropic's experience suggests that the future of engineering roles may involve less time spent on routine diagnostics and more time on high-level design, system integration, and ethical considerations—areas where human oversight remains critical. The company's decision to adapt its hiring test is a pragmatic response to this shift, recognizing that the skills needed for a performance engineer in 2026 are not the same as those needed in 2020.

As AI models continue to improve, we can expect to see similar challenges across other technical domains. The lesson from Anthropic is clear: assessments must evolve in tandem with the capabilities of the tools they are designed to evaluate. By releasing their original test, Anthropic is not just sharing a piece of their hiring process; they are contributing to a broader conversation about how to build and evaluate technical talent in an age of increasingly capable AI.