Machine Learning Researcher – Audio

Grupo Protege

Machine Learning Researcher focusing on researching audio data quality at Protege. Leading evaluation and optimization of speech datasets for AI training.

Posted 6/1/2026full-timeRemote • 🇧🇷 BrazilMid-LevelSeniorWebsite

ATS Keywords

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Hard Skills

machine learningaudio signal processingspeech technologydata evaluationsaudio analysesbenchmarksmetrics developmentsignal propertiesdataset selection strategiesexperimental analysis

Soft Skills

excellent written communicationexcellent verbal communicationanalytical skillshypothesis formulationexperiment executionresult analysisscalable tool developmentdecision-makingresearch explorationproduction implementation

Certifications & Qualifications

PhDMaster’s degree

Industry Keywords

audio data qualityquality scorecardsacoustic propertiesspectral energy distributionhigh-frequency roll-offnoiseclippingreverberationdistortioncodec artifacts

About the role

Key responsibilities & impact

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.
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.
Build workflows that evaluate diarized or segmented speech regions, surfacing localized degradation that file-level averages may miss.
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.
Translate research findings into reproducible filtering rules, quality gates, and dataset selection strategies that improve dataset consistency across training runs.

Requirements

What you’ll need

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.

Benefits

Comp & perks

High ownership and bias toward action
Collaboration with external partners
Resourceful and resilient work environment