AI Benchmark Engineer - Native Language Specialist - Arabic
Egypt (Remote)ContractMiddle
Salary not disclosed
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Job Details
- Languages
- Arabic, English
- Experience
- 5+ years
- Required Skills
- Python
Requirements
- 5+ years of industry experience in software engineering.
- Proven track record at leading technology companies and/or graduation from top-tier engineering universities.
- Native or near-native fluency in Arabic, with a deep understanding of its grammar, register, and phrasing rules.
- High English proficiency.
- Strong proficiency in Python.
- Strong proficiency in standard shell scripting.
- Strong proficiency in data processing.
- Extensive experience with Terminal/CLI-based development workflows.
- Working familiarity with coding agents.
- Deep technical understanding of multilingual text processing pitfalls, including encoding/decoding robustness and Unicode normalization.
- Deep technical understanding of locale-dependent conventions (collation, casing, non-Gregorian dates).
- Deep technical understanding of text I/O, toolchain interoperability, and safe string operations.
- Deep technical understanding of Bidirectional/RTL handling, font fallbacks, and rendering/typography in UI or artifacts (for specific languages like Arabic).
Responsibilities
- Design, build, and validate Terminal-Bench tasks.
- Create high-signal, high-quality tasks to test multilingual environments.
- Evaluate Coding Agents.
- Build realistic task environments using datasets and files in your native language.
- Find failure points where AI does not work, in your native language.
- Support the development of robust solutions (reference implementations).
- Write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Sonnet, Opus).
- 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.
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