AI Research Scientist

• 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.

Machine Learning Researcher – Audio

• Design and build datasets, tasks, and environments for benchmarking agentic systems and multi-step model behavior.
• Translate real-world workflows into structured tasks, interaction traces, trajectories, stateful environments, and verifiable outcomes that can be used to evaluate advanced AI systems.
• Develop frameworks that assess diversity, realism, coverage, fidelity, informativeness, and downstream usefulness of datasets for agentic systems.
• Build quality scorecards and evaluation methods that make dataset strengths, weaknesses, and failure modes legible across teams.
• Evaluate planning, tool use, robustness, recovery from failure, task completion, and generalization behavior in RL-style or agentic environments.
• Connect model failures back to concrete dataset, environment, or task-design gaps and recommend improvements grounded in empirical evidence.
• Contribute to tools and systems that automate dataset validation, environment generation, rollout analysis, benchmark construction, and evaluation workflows.
• Improve internal infrastructure for reproducible experimentation, benchmark management, and evaluation quality.
• Collaborate closely with research and engineering teams to identify data bottlenecks, improve evaluation methodology, and shape internal best practices around task-grounded AI training data.
• Represent DataLab’s perspective in cross-functional discussions around dataset quality, benchmark design, and frontier agentic-system evaluation.

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Machine Learning Researcher – Audio

Machine Learning Researcher – RL and Agentic Systems