At xAI I processed large conversational datasets for Grok voice-model training, producing publication-grade vocal stems and reducing downstream transcription errors.

Key Achievement: Processed 2007 conversational data tasks with <0.1 % residual noise floor, directly contributing to Grok’s natural-sounding voice release.

Processed dialogue sample 1

Processed dialogue sample 2

Duties included:

* Analyzed raw conversational audio datasets collected for Grok voice-model training, identifying noise, artifacts, distortions, and speech imperfections across diverse speakers, accents, and recording environments.

* Performed forensic-level enhancement, cleaning, repair, and polishing of audio waveforms using industry-standard DAWs (e.g., Logic Pro, Izotope RX) and spectral editing tools to isolate and preserve vocal stems while eliminating background noise, hum, clicks, plosives, reverb, and compression artifacts.

* Applied targeted EQ, dynamic processing, de-essing, phase alignment, and manual waveform retouching to achieve maximum clarity, intelligibility, and natural timbre in every vocal delivery.

* Normalized levels, segmented utterances, and metadata-tagged files to meet xAI’s strict AI-training specifications, ensuring zero loss of phonetic nuance or emotional prosody.

* Delivered production-ready, publication-grade vocal stems that enabled Grok to synthesize speech in a fully clear, artifact-free tone across all output modes.

* Collaborated with data engineers and ML researchers to iterate on quality benchmarks, reducing downstream transcription errors and model hallucination caused by audio impurities.