VLIW / TPU / Kernel Optimization Assignment

OAAnthropicLast reported December 2025Medium Frequency
Reported
4× across candidate reports
First seen
April 2025
Last reported
December 2025
Reported outcome
mixed

Problem Overview

Take-home coding assignment (2–4 hours, AI usage permitted) for Anthropic's acceleration/performance engineering roles. You are given a Python-based VLIW or TPU simulator where each operation type (load, ALU, etc.) costs a different number of cycles. Your goal is to optimize a core kernel function to maximize speedup over the baseline…

  • 4 candidate-reported follow-ups — the exact probes interviewers asked, with the trigger for each
  • The rest of the problem statement — full requirements, constraints, and edge cases
  • Approach and trade-offs — what passing candidates did, and the mistakes that sink people
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Verified Source
Every question is reconstructed from multiple independent candidate reports. Verbatim follow-ups, not invented ones.
Codex Fact-Checked
Technical claims, formulas, and scale numbers are reviewed against primary sources.
Interviewer Follow-ups
The exact follow-ups reported by candidates, with the trigger that prompts each one — plus the mistakes that sink people.
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