Design, build, and operate scalable backend services and APIs that process and serve scholarly content Build and maintain data pipelines handling high-volume ingestion, transformation, and indexing Build the services and APIs behind user-facing product experiences, working closely with design, product, and frontend engineers Design search and discovery systems that return the right result, fast, across large corpora Build upstream tooling for peer review and editorial workflows Integrate LLMs and other AI capabilities into existing high-volume systems, with the same rigor around latency, cost, and failure handling you''d apply to any dependency Evaluate where AI meaningfully improves a product experience and where a conventional approach is the better engineering call Own systems in production: instrument, monitor, debug, and optimize for performance, reliability, and cost Make architectural decisions and document the tradeoffs behind them Review code, mentor other engineers, and raise the bar for engineering quality across the team Collaborate with product and engineering peers to translate user needs into production systems. 7+ years of professional software engineering experience Strong proficiency in a modern backend language Python, Go, Java, C#, or similar and the ability to pick up others Demonstrated experience building and operating production systems at scale Solid grounding in distributed systems: concurrency, caching, queuing, failure modes, and how systems behave under load Strong SQL and relational data modeling skills Experience building search over large corpora indexing, relevance, and ranking BS in Computer Science or equivalent practical experience.