Machine Learning Researcher, Audio

What You’ll Do

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

Translate research findings into reproducible filtering rules, quality gates, and dataset selection strategies that improve dataset consistency across training runs.

Build scalable tools and pipelines for applying audio quality analyses across large datasets, tracking results over time, and making quality signals accessible to researchers, engineers, and data teams.

Cross-functional collaboration

Work closely with ML researchers, data engineers, data operations, and external partners to define, measure, and communicate the value of Protege’s audio data assets.

What Success Looks Like

Near-term: establish a trustworthy audio-quality baseline

Create a trustworthy view of the quality, consistency, signal fidelity, and training-readiness of Protege’s speech and audio datasets, supported by metrics and scorecards the team can operationalize.

Then use targeted evaluations, ablations, and downstream model analysis to connect audio-quality issues to concrete dataset improvements and clearer prioritization over time.

What You Bring

PhD or equivalent Master’s degree + 4+ years industry experience in machine learning, audio signal processing, speech technology, computer science, statistics, engineering, or a related quantitative field.

Proven experience designing and running data evaluations, audio analyses, benchmarks, ablations, or slice-based analyses.

Strong understanding of speech/audio data and signal properties, including sampling rates, codecs, bandwidth, spectrograms, reverberation, clipping, noise, and perceptual quality.

Experience developing or critically evaluating metrics, benchmarks, or measurement frameworks for ML systems, data quality, speech technology, or audio signal analysis.

Ability to connect low-level signal properties to downstream machine learning behavior, including model accuracy, robustness, representation quality, speaker consistency, or synthesis quality.

Comfortable moving between research exploration and production implementation: you can formulate hypotheses, run experiments, analyze results, and turn findings into scalable tools or decision rules.

Excellent written and verbal communicator; able to write concise technical docs and explain empirical results clearly.

High ownership and bias toward action; you independently scope questions, design experiments, and drive them to decisions.

Bonus Signals

• Experience with ASR, TTS, speaker modeling, self-supervised speech models, diarization, or multimodal audio models.
• Experience developing evaluation frameworks or performance metrics for training data.
• Experience inventing, adapting, or validating audio quality metrics for ML training datasets.
• Experience studying the relationship between dataset quality and downstream model performance.
• Publications or open-source contributions in speech, audio ML, data-centric AI, ML evaluation, or related areas.
• Cross-functional collaboration with product, infrastructure, data operations, or partnership teams.
• Experience collaborating with industry or academic labs on speech/audio research or data projects.

Protege ValuesPass the Loved Ones’ Test

We act with integrity and do the right thing — especially when it’s hard and no one is watching.

Always Find a Way

We are resourceful, resilient builders who solve hard problems and push through obstacles.

Go Fast and Grow Fast

Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.

Practice Kindness and Candor

We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.

Deliver Together

We win as one team. Collaboration, accountability, and shared ownership drive our success.

Own the Outcome. Hone the Craft.

We take pride in our work, sweat the details, and continuously raise the bar for excellence.