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.

Role Overview Data is the foundation of AI performance, and we believe model quality starts with data quality. For video and multimodal models in particular, the bar for data precision, coverage, and curation is even higher.

We’re seeking a Senior Researcher focused on Diffusion Models or Vision Models to lead the evaluation and optimization of large-scale datasets used to train generative video models. You will be responsible for building the systems, processes, and standards that ensure Protege’s video data is diverse, representative, high-impact, and ready for production-grade training. You’ll run targeted evaluations, characterizing our video assets, and creating the technical artifacts (metrics, benchmarks, internal scorecards) that define, measure, and communicate the value of our video data assets.

This is an ideal role for someone who is deeply obsessed with video data quality and understanding, comfortable operating in both strategic and hands-on modes, and excited to help Protege become the ubiquitous platform for video training data.

Key Responsibilities

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

About You

• PhD or equivalent Master’s degree + 4+ years industry experience in machine learning, computer science, statistics, engineering, or a related quantitative field, with a focus on diffusion models or video data
• Proven experience designing and running model evaluations and data analyses (benchmarks, ablations, slice‑based analyses)
• Ability to understand data impact on models at various stages of the training stack.
• Excellent written and verbal communicator; able to write concise technical docs and explain empirical results clearly
• High ownership and bias toward action; you independently scope questions, design experiments, and drive them to decisions

Bonus if you have these attributes

• Publications or open-source contributions in data-centric AI or related areas
• Experience developing evaluation frameworks or performance metrics for training data
• Cross-functional collaboration with product, infrastructure, or partnership teams
• Prior work on video generation or understanding models and their datasets or benchmarks
• Experience collaborating with industry or academic labs on video research or data projects

Protege Values

• We pass the loved ones' test — integrity isn't negotiable, even when it's costly
• We always find a way — obstacles are expected, giving up isn't
• We go fast and grow fast — velocity is a competitive advantage and we treat it that way
• We practice kindness and candor — hard conversations happen here, and they happen with care
• We deliver together — no silos, no lone heroes, no passengers
• We own the outcome — full accountability, continuous improvement, mastery over time