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Protege is building a platform to solve AI's data problem by facilitating secure, efficient, and privacy-centric exchange of AI training data. This Forward Deployed Engineer role focuses on the video vertical, combining customer-facing technical leadership with platform engineering.
You will own the technical success of video customers end-to-end, from feasibility assessment through post-delivery support. This includes translating model-development goals into executable technical plans, implementing and operating customer engagements, and managing multiple concurrent deals. A key responsibility is building the measurement and curation layer that transforms raw video footage into describable, sellable datasets—including metadata enrichment, quality checking, and rapid characterization of unknown datasets.
You'll work closely with the video vertical's GM, product and platform engineering teams, the Data Lab, and commercial stakeholders. The role bridges customer requirements with internal platform capabilities, identifying patterns from customer work that should become shared cross-vertical features. You'll establish technical playbooks and quality standards for the video function to enable scaling.
In the first 30 days, you'll learn Protege's platform, existing video catalog, customer portfolio, and processing systems while pairing on a live deal. By 60 days, you'll operate an active video deal independently and build or extend tooling from customer requests. By 90 days, you'll serve as the default technical owner across the video vertical's active portfolio, own end-to-end architecture and delivery, establish the video FDE playbook, and maintain a roadmap of platform investments.
Required: 3+ years as an engineer with meaningful customer or external stakeholder exposure; direct experience with media data (video preferred); experience building and operating data processing/analysis/delivery systems at scale; strong customer-facing ability including translating ambiguous requirements and building trust; demonstrated end-to-end ownership from problem definition through implementation and support; high ambiguity tolerance and bias to action; comfort with fast-paced environments and multiple concurrent priorities.
Preferred: hands-on video processing at scale (codecs, transcoding, ffmpeg, shot detection, frame sampling, perceptual quality); early-stage or founding engineer experience; Python and SQL; search, vector embeddings, semantic retrieval, or ML-assisted curation; experience evaluating or deploying vision-language models.
