Machine Learning Researcher, Audio

Company Overview

We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy‑centric exchange of AI training data.

Solving AI’s data problem is a generational opportunity. We’re backed by world‑class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.

We’re a lean, fast‑moving, high‑trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.

Data is the foundation of AI performance, and we believe model quality starts with data quality. For speech and audio models in particular, the bar for signal fidelity, consistency, and quality control is exceptionally high.

We’re seeking a Machine Learning Researcher focused on audio data quality, ML data evaluation, and quality control to lead the evaluation and optimization of large‑scale speech datasets used to train audio, speech, and multimodal models. This role will be responsible not only for applying existing audio quality metrics, but also for researching how audio data quality should be evaluated for machine learning systems and developing new methods, benchmarks, and evaluation frameworks that better predict downstream model performance.

You will help define what “high‑quality audio data” means in the context of modern ML training. That includes studying how different forms of acoustic degradation, dataset inconsistency, recording conditions, speaker variation, labeling quality, segmentation quality, and signal artifacts affect model behavior across ASR, TTS, speaker modeling, representation learning, and multimodal systems.

A core part of this role will be original research and method development: designing new approaches for measuring audio data quality, validating those approaches against downstream model outcomes, and translating research insights into practical evaluation tools, filtering rules, and quality standards used across Protege’s data platform.

This is an ideal role for someone deeply obsessed with audio data quality and signal understanding, comfortable operating in both research and hands‑on implementation modes, and excited to help Protege become the ubiquitous platform for high‑quality AI training data.

Research audio data quality for machine learning

Investigate how audio quality, signal properties, dataset composition, and localized acoustic issues affect downstream model training, evaluation, and deployment.

Develop new metrics, benchmarks, diagnostics, and evaluation frameworks for measuring audio data quality in ways that are predictive of ML model performance.

Speech dataset characterization and metrics

Analyze and summarize Protege’s audio catalog and maintain clear, up‑to‑date quality scorecards and metrics for key speech datasets.

Develop methods to measure true acoustic properties directly from the waveform, including effective bandwidth, spectral energy distribution, high‑frequency roll‑off, noise, clipping, reverberation, distortion, and codec artifacts.

Segment‑level quality evaluation

Build workflows that evaluate diarized or segmented speech regions, surfacing localized degradation that file‑level averages may miss.

Apply multiple complementary quality metrics to detect bandwidth mismatches, resampling artifacts, clipping, reverberation, codec distortion, and other forms of degradation.

Model and data evaluation

Design and run targeted evaluations connecting audio quality issues to downstream model behavior, including ASR performance, speaker embedding stability, learned speech representations, and synthesis quality.

Test which audio quality metrics meaningfully correlate with model outcomes, identify failure modes of existing metrics, and design better alternatives when current approaches are insufficient.

Deterministic filtering and evaluation infrastructure

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