xAI AI Tutor - German at Xai is responsible for training Grok in multilingual voice interactions and speech recognition. The role includes curating and annotating audio data to improve AI's handling of diverse languages and accents.
Responsibilities
As an AI Tutor specialized in multilingual audio capabilities, you will contribute to SpaceXAI's mission by training and refining Grok to excel in voice interactions, speech recognition, and auditory experiences across diverse languages, accents, and cultural contexts. Your work will focus on curating and annotating high-quality audio data to enhance Grok's global accessibility, enabling natural spoken interactions for users worldwide, bridging language barriers through accurate speech processing, and improving the AI's handling of multilingual audio nuances.
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 (such as intonation, rhythm, and accent), 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.
Qualification
Strong communicationBackground in linguistics (eProfessional experience in voice work
Required
Native proficiency in German with exposure to diverse accents, dialects, or regional variations.
Proficiency in English (minimum B2 level) with clear, natural vocal delivery and pronunciation suitable for audio recording purposes.
Strong auditory perception to identify nuances in speech, accents, pronunciation, intonation, and audio quality across languages.
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, with the ability to articulate audio-related feedback effectively.
Commitment to developing AI that masters sophisticated multilingual audio capabilities.
Preferred
Demonstration of exceptional attention to linguistic nuance, auditory detail, and data quality beyond standard transcription work.
Deep understanding and taste of what good/useful Audio data is.
Strong command of advanced transcription and annotation practices, including handling disfluencies, accents, and prosodic features (intonation, stress, rhythm, emotion, etc) with high consistency and accuracy.
Background in linguistics (e.g., phonetics, phonology, sociolinguistics), speech sciences, cognitive science, or a related field, or equivalent practical experience, with demonstrated ability to analyze accent variation, pronunciation differences, and multilingual speech patterns.
Experience working with speech/audio datasets, annotation workflows, or AI training data, including knowledge/experience with training voice models, and an understanding of how data quality impacts model performance.
Professional experience in voice work, including voice acting, voice recording, podcasting with a measurable audience (e.g., X following), or similar audio production demonstrating attention to clarity and recording quality.
Demonstrated ability to exercise independent judgment in ambiguous audio scenarios and make consistent, defensible annotation decisions.
Portfolio (strongly preferred for advanced candidates): Voice samples, annotated transcripts, or audio-related work demonstrating quality, methodology, and attention to detail.
