[Hiring] Machine Learning Researcher - Audio @Protege

Role Description

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 for:

• Applying existing audio quality metrics.
• Researching how audio data quality should be evaluated for machine learning systems.
• 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, including:

• Studying how different forms of acoustic degradation affect model behavior.
• Analyzing dataset inconsistency, recording conditions, speaker variation, labeling quality, segmentation quality, and signal artifacts.

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.
• 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.

Qualifications

• 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.
• Excellent written and verbal communicator; able to write concise technical docs and explain empirical results clearly.
• High ownership and bias toward action; independently scope questions, design experiments, and drive them to decisions.

Requirements

• 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.

Benefits

• Competitive salary and equity options.
• Comprehensive health, dental, and vision insurance.
• Flexible work hours and remote work options.
• Professional development opportunities.

Company Description

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.

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.