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Protege is building a platform to solve AI's biggest unmet need: secure, efficient, and privacy-centric access to high-quality training data. The company is backed by world-class investors and already powers partnerships with leading AI teams.
DataLab is Protege's research arm—a team of research scientists tackling fundamental challenges in data for AI. The lab bridges research theory and data deployment, publishing on critical questions: what agentic AI should be trained to do, how to quality-control large-scale corpora, and how to build evaluation datasets that reflect real-world performance.
As Head of Research, you will lead DataLab as a strategic research function focused on answering the hardest questions about data for AI. This is both a leadership and builder role. You will define the research agenda, build rigorous systems for experimentation and evaluation, and ensure DataLab's work directly informs product direction, customer strategy, and platform capabilities.
Key responsibilities include:
- Define and lead research strategy for DataLab, 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 evaluating 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 DataLab in sales, technical discovery, delivery, and customer strategy conversations
- 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
- 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
Success milestones: In 30 days, you will build deep understanding of Protege's platform, customer workflows, and research priorities while auditing current DataLab workflows. In 60 days, you will deliver a clear research roadmap aligned to company priorities and launch initial experiments with standardized processes. In 90 days, you will deliver research insights that directly influence product roadmap decisions and establish DataLab as a trusted strategic partner.
Requirements:
- Led impactful research initiatives in AI, machine learning, data infrastructure, or applied research environments where outcomes influenced product or business strategy
- Built and managed high-performing research teams that operated with autonomy, technical rigor, and fast execution cycles
- Experience partnering directly with customers, GTM teams, or external stakeholders in applied technical settings, with judgment to represent both research and business value of complex AI/data work
- Developed frameworks for evaluating model performance, dataset quality, synthetic data, or large-scale experimentation systems
- Experience operating in ambiguous, fast-moving environments where priorities evolved quickly and execution mattered
- Strong ability to communicate complex technical findings to both technical and non-technical audiences
- Demonstrated ownership of cross-functional initiatives spanning research, engineering, product, and go-to-market teams
- Track record of translating research into practical systems, tools, or customer-facing impact
- Experience working with modern AI systems, foundation models, LLM evaluation workflows, or privacy-centric data methodologies
- High judgment, strong prioritization skills, and comfort making decisions with incomplete information
- Motivated by building from first principles and solving foundational problems in AI infrastructure
