Machine Learning Engineer, Core Data
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Job Description
ML Engineer focused on data quality for Cantina's social AI. Auditing, denoising, and transforming datasets for speech systems.
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.
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.
Benefits:
- Health insurance
- Professional development opportunities
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