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.

Machine Learning Researcher – RL and Agentic Systems

• Design advanced computer science problems focused on reasoning and conceptual understanding
• Create structured reference solutions with clear logic and technical explanations
• Evaluate AI-generated answers for correctness, reasoning quality, completeness, and clarity
• Identify edge cases, logical flaws, and weak reasoning patterns in model outputs
• Review complex topics across algorithms, systems, databases, networking, security, and theory
• Contribute to benchmark and evaluation quality improvements
• Provide detailed feedback to support AI model training and evaluation
• Collaborate with researchers and reviewers on problem refinement
• Maintain high-quality standards and consistent evaluation methodology

PhD Computer Science Expert – AI Research

• Drive innovation in model serving and inference architectures for advanced AI systems.
• Focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real-world applications.
• Work on a wide spectrum of systems, ranging from resource-efficient models designed for limited hardware environments to complex, multi-modal architectures that integrate data such as text, images, and audio.
• Adopt a hands-on, research-driven approach to develop, test, and implement novel serving strategies and inference algorithms.
• Engineer robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments.
• Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios.

AI Research Engineer – Kernel, Inference Optimization

• Architect and orchestrate massively parallel AI research workflows.
• Design, train, and iterate on models across the full GenAI stack.
• Conduct rigorous, first-principles research into model architectures, training dynamics, reinforcement learning, and knowledge representation.
• Amplify your expertise across NLP, computer vision, multimodal understanding, agentic reasoning, and domain science.
• Develop and contribute to shared tooling, libraries, and platforms that enable autonomous experiment pipelines.
• Collaborate with engineering, product, and domain experts to integrate breakthroughs into the platform rapidly.
• Document findings, publish at top-tier venues, and build internal knowledge systems.

See all jobs on JobTailor

Machine Learning Researcher – Audio

Machine Learning Researcher – RL and Agentic Systems

PhD Computer Science Expert – AI Research

AI Research Engineer – Kernel, Inference Optimization

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