cantina - Machine Learning Engineer, Core Data
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Requirements
• Strong experience building ML-driven data quality systems for audio/speech, or equivalent data-centric ML experience with a track record of improving model outcomes via better data. • Proficient in Python and PyTorch; training/finetuning SSL-ASR (Whisper, Wav2Vec, BERT) models, CNN based classifiers and writing robust production code. • Audio/speech fundamentals: torchaudio/librosa/ffmpeg, spectrogram features (e.g., log-mel, MFCC), VAD/SAD, basic DSP, and audio QA. • Scalable data engineering skills: Spark/Beam or similar, SQL, Airflow or equivalent orchestration, and cloud storage/computing (AWS/GCP). • Familiarity with ASR/TTS metrics and tooling: WER, MOS/MOSNet, PESQ/STOI/ViSQOL, speaker verification (EER), diarization, language ID. • Experience with dataset validation, versioning, and experiment tracking; comfort debugging data issues from single samples to fleet-wide trends. • Ability to balance rigor with speed, and to translate ambiguous requirements into measurable data improvements. • Shipped datasets and/or data quality tooling that moved the needle for TTS/ASR/VC in production. • Built and deployed classifiers for LID, SV/diarization, VAD, noise/glitch detection, or safety/content moderation for audio. • Ran crowdsourcing/vendor annotation at scale with strong quality control (honeypots, IAA, label aggregation). • Background in de-noising/enhancement and their effects on downstream TTS quality. • Contributions to open-source or publications in speech/audio/ML. • Experience with data governance, consent tracking, and policy enforcement.
Responsibilities
• Dataset ownership: define specs; audit and curate large-scale audio/text; close corpus gaps and fix sample-level issues. • Quality instrumentation: build automated gates/metrics (e.g., SNR, clipping, VAD, WER, SV/LID, safety) with dashboards; validate against listening tests. • Classifiers and filters: train lightweight models to tag, score, and filter data (VAD, ASR gating, LID, SV/diarization, noise/safety); calibrate to subjective outcomes. • Cleaning and integrity: apply denoise/dereverb/de-clip when beneficial; deduplicate and decontaminate; prevent leakage; maintain lineage and versioned releases. • Data selection: optimize mixtures via sampling, weighting, curriculum, and active learning; mine hard negatives and long-tail cases. • Tooling and pipelines: ship reproducible ETL and validation; integrate quality gates into training/eval; add monitoring and alerts. • Human-in-the-loop and compliance: run MTurk/vendor annotation with strong QC; ensure consent/licensing/policy compliance; collaborate across teams and document datasets.
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