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About The Opportunity We are building a rigorous, verifiable evaluation suite of Terminal-Bench tasks designed to test the limits of large language models on multilingual software challenges. Our goal is to measure multilingual robustness across prompt language effects, non-English data processing, and complex locale/encoding edge cases in terminal workflows. We are seeking experienced native-speaking software engineers to design, build, and validate these benchmarks. You will create high-signal, high-quality tasks that genuinely test a model's ability to handle multilingual environments without relying on English translation crutches. Note this is a remote, freelance opportunity What You’ll Deliver Task Engineering: Evaluating Coding Agents. Asset Creation: Build realistic task environments using datasets and files in your native language. Crucially, these assets must remain in the target language to genuinely measure multilingual handling. Prompting & Translation: finding failure points where AI does not work, in your native language Implementation & Verification: Support the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary). Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Sonnet, Opus). Quality Assurance: Participate in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity. Qualifications Experience: 5+ years of industry experience in software engineering. Background: Proven track record at leading technology companies and/or graduation from top-tier engineering universities. Language: Native or near-native fluency, with a deep understanding of its grammar, register, and phrasing rules. High English proficiency. Technical Stack: Strong proficiency in Python, standard shell scripting, and data processing. Workflow: Extensive experience with Terminal/CLI-based development workflows and a working familiarity with coding agents. Domain Expertise: Deep technical understanding of multilingual text processing pitfalls, including: Encoding/decoding robustness and Unicode normalization. Locale-dependent conventions (collation, casing, non-Gregorian dates). Text I/O, toolchain interoperability, and safe string operations. (For specific languages) Bidirectional/RTL handling, font fallbacks, and rendering/typography in UI or artifacts.
