Grupo Protege

• 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.

• 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 DataLab 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.

• Design tasks and benchmarks that distinguish capability levels across frontier models — including agentic, reasoning-heavy, and domain-specific (healthcare, finance, scientific) settings. • Validate evaluations rigorously: run human baselines, analyze inter-rater reliability, study how elicitation and scaffolding shift results, and quantify what’s signal versus noise. • Develop the “science of evals” at Protege — including item response theory, contamination analysis, predictive validity studies, and statistical frameworks for comparing models with appropriate uncertainty. • Run evaluations on current frontier models, sometimes in collaboration with partners at AI labs, enterprises, and government. • Publish research that establishes Protege as the standard-setter for evaluation data, and contribute to the broader AI community’s understanding of what good evals look like. • Translate findings into product, working closely with the data and engineering teams to turn research into evaluation datasets customers can deploy. • Partnering with outsourced annotation vendors - Evaluation data is only as good as the people producing it. A meaningful share of this role is owning the statistical machinery that determines which annotators we trust, on which tasks, and by how much — and translating that into trustworthiness scores Protege’s customers can rely on.

• Own a revenue target, working closely with Protege’s Video GM and team • Build, manage, and close a pipeline of opportunities across new and existing accounts • Balance self-generated pipeline with inbound and outbound-supported opportunities • Identify and develop opportunities where video data is required for AI model training, fine-tuning, or evaluation • Engage data science, product, and business stakeholders to understand data needs • Position Protege’s capabilities as solutions to data quality, scale, and availability challenges • Run full-cycle sales from discovery through close • Translate technical requirements into clear commercial scopes and proposals • Navigate multi-stakeholder environments including AI, legal, content, and product teams • Build a strong point of view on how video data is used in AI/ML workflows • Identify repeatable use cases and patterns within the video vertical • Contribute to go-to-market strategy and offerings • Work closely with GM, Delivery, Data Lab, and Partner teams to scope and execute solutions • Partner with Product and Commercial Ops to improve packaging, pricing, and sales motion • Collaborate with outbound/BDR teams on targeted account strategies

• Help design, construct, and validate complex healthcare data cohorts used for AI model training • Act as a technical partner for complex data problems, including cohort construction and data validation • Translate research and customer requirements into practical dataset definitions • Build SQL and analysis needed to create datasets • Collaborate with delivery engineers to implement solutions requiring data pipeline changes • Validate datasets for quality and acceptance criteria • Work with AI researchers to translate goals into practical dataset specifications • Analyze partner datasets for schema understanding and data quality

• Analyze and summarize Protege’s video catalog and maintain clear, up-to-date data scorecards and metrics for key datasets • Design and run targeted evaluations of video models, connect failures to concrete data gaps, and test which data changes actually improve model performance • Define and maintain eval sets and slices that reflect real-world video use cases and stress-test models in meaningful ways • Build and maintain relationships with external researchers and labs to surface emerging video data needs and model pain points

• Mature the Security & Compliance Program • Audit and improve the existing security program by identifying gaps, prioritizing improvements, and bringing more structure to what exists. • Formalize security policies and frameworks appropriate for our stage • Own and evolve our compliance posture. We have SOC 2 Type II in place and you'll maintain it, improve our controls, and provide automation wherever needed • Ensure compliance with HIPAA and other healthcare data regulations, and build a robust PHI protection program • Protect the Data Pipeline • Secure the end-to-end lifecycle of training data which includes ingestion, processing, storage, preparation, and delivery • Partner with engineering to embed security into CI/CD pipelines, cloud infrastructure, and data workflows • Be Technical and Hands-On • Conduct threat modeling, architecture reviews, and code-level security assessments • Lead incident response when things go wrong • Evaluate and deploy security tooling • Enable the Business • Translate security risks into business language for the executive team and board • Serve as the security face to customers, fielding security questionnaires, supporting sales cycles, and building trust with AI company partners and customers • Build a security-aware culture across the company through training and lightweight processes that don't slow teams down • Scale the Function • Decide what to build, what to buy, and what to outsource • Set the roadmap for how security evolves from Series A through a rapid growth stage

• Own cross-cloud data movement and delivery • Execute and monitor large-scale data transfers across AWS S3, Google Cloud Storage, Azure Blob, Snowflake, and customer environments. • Use CLI tools such as rclone, s5cmd, and cloud-native utilities to move data safely and efficiently. • Manage credentials, permissions, manifests, and delivery packaging artifacts required for ingestion, subset delivery, and handoff workflows. • Build structured data assembly and lightweight transformation workflows • Use Python and SQL to join datasets, add derived columns, clean data, and validate CSV, Parquet, and database tables. • Support customer-specific assembly work that turns raw inputs into delivery-ready datasets. • Apply a high bar for data integrity, structure, and reproducibility before handoff. • Operate internal pipelines with production discipline • Leverage Protege's Dagster-based platform to orchestrate data processing and delivery. • Maintain clean separation between pre-production and production workflows and validate configs before runs. • Build lightweight scripts and command-line workflows for filtering, manifest generation, validation, and recovery. • Create leverage for the team and platform • Document steps, outputs, and recovery paths for auditability and repeatability. • Identify recurring delivery patterns, failure modes, and manual toil. • Partner with Engineering to turn one-off operational work into repeatable platform capabilities and test new tooling before go-live.

• Lead onboarding of new healthcare data partners, establishing repeatable operational playbooks • Coordinate secure data access, querying workflows, and transfer mechanisms • Partner with Product and Engineering to align on ingestion, normalization, and technical setup • Ensure privacy, compliance, and governance requirements are properly implemented in collaboration with Legal and Compliance • Develop structured processes to scale partner onboarding efficiently • Conduct structured evaluations of partner datasets across EHR, imaging, claims, genomics, device, or real-world data • Partner with DataLab to assess data completeness, quality, modality coverage, and fitness for AI training use cases • Collaborate with scientific teams to support feasibility assessments for customer projects • Identify data gaps, operational risks, and areas requiring remediation • Establish partner performance scorecards tracking responsiveness, reliability, and data quality • Align partners on project requirements, qualification criteria, and delivery timelines • Track feasibility analyses and ensure timely, accurate partner responses • Monitor deal progress from qualification through fulfillment • Coordinate data delivery milestones and resolve execution bottlenecks • Proactively surface risks to internal stakeholders and propose mitigation plans • Ensure partners are fulfilling their obligations across individual customer engagements • Monitor utilization, revenue contribution, and operational performance • Partner with the Strategic Partnerships Lead to identify expansion opportunities • Maintain aligned communication between partners and internal teams • Continuously improve operational workflows to increase reliability and speed • Serve as the primary execution point of contact for healthcare data partners • Build trusted relationships grounded in transparency, responsiveness, and accountability • Reinforce Protege’s position as the premier platform for responsible AI data collaboration

• Own the privacy product from discovery through execution • Drive discovery to identify where Protege can create the most meaningful privacy and trust capabilities across different data types and customer contexts. • Synthesize signals from customers, data partners, and internal teams to define what should be built first and why. • Take the first wedge from problem definition to pilot-ready scope. • Build and manage the vendor and partner network • Maintain a current view of the privacy-preserving tooling and partner landscape, including strengths, limits, cost, and modality fit. • Use that landscape knowledge to make principled build-versus-buy decisions rather than defaulting to familiar tools or one-off requests. • Structure external relationships in a way that preserves flexibility as requirements evolve. • Translate research into product • Partner closely with Data Lab to identify which privacy-related research directions have real near-term product potential. • Decide what is robust enough to operationalize, what needs more validation, and what is promising but not yet product-ready. • Build the bridge from proof-of-concept to a capability we can deliver at scale. • Navigate a complex external stakeholder map • Build credibility with customers, data partners, and third-party certifiers who care about how data is handled and what claims Protege can responsibly make. • Communicate clearly to different audiences without over-claiming or talking past their real concerns. • Bring external feedback back into product scope in a form that helps the team make better decisions. • Make and defend scoping calls • Hold disciplined focus in a problem space that can expand infinitely across modalities, regulations, and risk tolerances. • Know which problem to solve first, make that case clearly, and resist broadening scope before the foundation is proven.