Head of AI DataLab Research

Company Overview

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. The company that succeeds will be one of the largest in AI — and in tech.

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.

Data Lab is Protege's research arm — a team of research scientists committed to tackling the fundamental challenges and open questions regarding data for AI. We bridge the gap between research theory and data deployment to push the frontier forward, publishing on the questions that matter: what agentic AI should actually be trained to do, how to quality‑control large‑scale corpora, and how to build evaluation datasets that reflect the real world rather than the leader board.

The Director of Research will lead Protege's Data Lab as a strategic research function focused on answering the hardest questions about data for AI. This person will define the research agenda, build rigorous systems for experimentation and evaluation, and ensure Data Lab's work directly informs product direction, customer strategy, and platform capabilities.

This is both a leadership and builder role. The right person can set a clear research roadmap, manage and scale a high-performing team, partner across Product, Engineering, GTM, and Leadership, and translate ambiguous technical questions into practical frameworks, measurable experiments, and actionable recommendations.

The Director of Research will work closely with GTM across sales, delivery, and partner conversations — bringing technical credibility into the room when it matters and representing Protege’s research perspective externally.

• Define and lead the research strategy for Protege's Data Lab, aligning experimentation with company priorities and product direction.
• Partner closely with Product, Engineering, and GTM teams to identify high‑value research opportunities tied to AI data quality, evaluation, and marketplace performance.
• Design and oversee experiments that evaluate dataset quality, model performance, synthetic data workflows, and privacy‑preserving methodologies.
• Build scalable systems for benchmarking, labeling quality analysis, and training data evaluation across multiple AI modalities.
• Serve as a customer‑facing research partner to GTM
  , representing Data Lab in sales, technical discovery, delivery, and customer strategy conversations while translating research depth into practical guidance that helps Protege deliver value to our partners and customers.
• Translate ambiguous technical questions into clear research frameworks, measurable hypotheses, and actionable recommendations.
• Publish internal research findings that directly influence product decisions, customer strategy, and platform capabilities.
• Lead, manage, and scale a high‑performing team of researchers and data scientists, driving execution, technical excellence, and career development across the Data Lab organization.
• Establish operational rigor around experimentation, reproducibility, and research documentation.
• Represent Protege externally through technical conversations with customers, partners, and the broader AI ecosystem.
• Stay at the forefront of advancements in foundation models, evaluation methodologies, data infrastructure, and AI alignment research.

• Build a deep understanding of Protege's platform, customer workflows, and current research priorities.
• Meet cross‑functional stakeholders across Product, Engineering, GTM, and Leadership to understand strategic goals and existing technical challenges.
• Audit current Data Lab workflows, experimentation…