Spanish Quality Assurance Lead (QAL)
Evaluate AI-generated Spanish language outputs for accuracy, fluency, and cultural appropriateness as a Quality Assurance Lead.
Evaluate AI-generated Spanish language outputs for accuracy, fluency, and cultural appropriateness as a Quality Assurance Lead.
Review and evaluate AI-generated physics content and solutions, ensuring scientific accuracy and reasoning quality across quantitative and conceptual tasks.
Evaluate AI-generated Portuguese language outputs for accuracy, fluency, and cultural appropriateness as a Quality Assurance Lead.
Lead quality assurance for AI-generated Python code, evaluating correctness, efficiency, and adherence to best practices.
Lead quality assurance for AI-generated SQL code, reviewing query accuracy, performance, and correctness across complex database tasks.
Review AI-generated accounting and finance responses as a Subject Matter Expert, evaluating bookkeeping logic, journal entries, accruals, reconciliations, financial statement presentation, and controls. Fact-check technical accounting claims and write model solutions demonstrating correct methods.
Review AI-generated marketing and content-writing outputs as a Subject Matter Expert, evaluating intent alignment, tone, brand fit, messaging, and audience targeting. Identify issues in logic, compliance risk, structure, and style; write model copy demonstrating strong copywriting technique.
Review AI-generated investment and banking outputs as a Subject Matter Expert, evaluating valuation logic (DCF/comps/precedents), deal structuring, capital markets concepts, accounting linkages, and risk analysis. Provide precise written feedback, fact-check financial claims, and write model solutions demonstrating correct methods.
Transcribe real-world Air Traffic Control (ATC) and pilot audio recordings with high accuracy for next-generation aviation AI systems. Label speakers, callsigns, instructions, and responses; correctly capture aviation terminology, abbreviations, and standard ATC phraseology in task-based assignments.
Design, build, and refine MuJoCo simulation environments that train AI systems to perform real-world robotic tasks — from locomotion and dexterous manipulation to multi-agent coordination. Work includes wrangling MJCF files, tuning reward functions, and debugging contact dynamics for reinforcement learning pipelines.
Design and implement coding benchmarks used to evaluate frontier AI models across real-world programming tasks. Build and maintain scalable data pipelines for AI evaluation workflows, analyze AI-generated code for correctness and edge-case failures, and create structured evaluation scenarios that rigorously test reasoning, debugging, and code quality across large multi-language codebases.
Provide native-level English language expertise to train and evaluate AI models through translation, annotation, and linguistic quality review.
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