xAI is seeking an AI Tutor specialized in Urdu to train and refine its Grok model for multilingual audio interactions. The role involves curating and annotating high‑quality audio data, providing voice recordings, and collaborating with technical staff to enhance speech recognition and multilingual processing capabilities. This remote position offers hourly compensation for U.S. candidates and varying compensation for international applicants.
AI Tutor - Urdu at xAI
Remote - Remote
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USD 35 - 45/hour
Requirements
Skills
- Native proficiency in Urdu
- Proficiency in English (minimum B2 level)
- Strong auditory perception to identify nuances in speech, accents, pronunciation, intonation, and audio quality
- Demonstrated ability to handle multilingual audio content, including evaluating speech accuracy, cultural vocal expressions, and contextual interpretation in spoken form
- Demonstrated ability to transcribe audio with high accuracy across accents and varying audio quality
- Comfort providing high-quality voice recordings and feedback on audio samples in multiple languages
- Strong comprehension skills and the ability to make independent judgments on ambiguous or varied audio material, including noisy or accented speech
- Strong communication, interpersonal, analytical, detail-oriented, and organizational skills
- Commitment to developing AI that masters sophisticated multilingual audio capabilities
Responsibilities
- Use proprietary software to provide labels, annotations, recordings, and inputs on projects involving multilingual audio clips, voice recordings, speech samples, and auditory elements in various languages
- Support the delivery of high-quality curated audio data that ensures clear, natural spoken output, accurate representation of linguistic and prosodic details, and professional audio standards
- Collaborate with technical staff to develop tasks that improve AI's ability to handle speech modulation, accent variation, noise in real-world recordings, and multilingual audio processing
- Work with technical staff to improve annotation tools for efficient audio workflows
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