Remote Freelance Multilingual AI Benchmark Engineer

LILT; United States; Remote; USD 83,000 - 152,000; Full time

Benefits offered by this job

Flexible schedule

Competitive pay

Meaningful projects

Global community

Portfolio growth

Job summary

Lilt is seeking experienced native-speaking software engineers to design, build, and validate multilingual benchmarks. You will create high-signal tasks and assets in your native language, keeping content in the target language to test multilingual handling.

This is a remote, freelance opportunity with flexible hours and competitive rates. Join a global team and contribute to cutting-edge AI language technology through rigorous quality checks, deterministic verifications, and rubric-based

Qualifications

• 5+ years of industry experience in software engineering.
• Native or near-native fluency in the target language with strong grammar and phrasing.
• High English proficiency.

Responsibilities

• Task Engineering: Evaluating Coding Agents.
• Asset Creation: Build real task environments using datasets/files in your native language.
• Prompting & Translation: identify failure points in your native language.
• Implementation & Verification: develop reference implementations and deterministic verifier scripts.
• Calibration & Execution: analyze logs and calibrate task difficulty across model tiers.
• Quality Assurance: participate in a 4-layer human quality control process and automated checks.

Skills

Python

Shell scripting

Data processing

Terminal/CLI workflows

Multilingual text processing

Education

Top-tier engineering universities

Job description

Lilt is seeking experienced native-speaking software engineers to design, build, and validate multilingual benchmarks. You will create high-signal tasks and assets in your native language, keeping content in the target language to test multilingual handling.

This is a remote, freelance opportunity with flexible hours and competitive rates. Join a global team and contribute to cutting-edge AI language technology through rigorous quality checks, deterministic verifications, and rubric-based