Backend Engineer
Build and optimize data pipelines for academic literature at scale
You will work across systems that ingest, process, store, and serve data from a literature database of 250M+ academic papers and user data accumulated over a decade. This includes building pipelines, optimizing search, handling PDFs at scale, and exposing clean APIs. The role focuses on ensuring data quality, correctness, deduplication, and consistency in production environments.
Why This Role?
Work on a literature database of 250M+ academic papers with real-world impact
Key Responsibilities
- Build and operate data ingestion pipelines handling messy, heterogeneous sources
- Deploy and operate services on AWS
- Design and maintain REST APIs for clean data exposure
- Optimize full-text search systems including indexing and query performance
- Process PDFs at scale including extraction, transformation, and storage
- Work with third-party data sources, APIs, and web scraping
Requirements
- Strong backend engineering background with experience in data-heavy systems
- Experience deploying and operating services on AWS
- Experience designing and maintaining data ingestion pipelines
- Comfort with web scraping and third-party data sources/APIs
- Familiarity with Node.js and TypeScript
- Solid understanding of full-text search systems including indexing and relevance tuning
Required Skills
Keywords
View Original Description from RemoteOK
Original description from RemoteOK
Paperpile runs on data at scale, with a literature database of 250M+ academic papers and a growing body of user data accumulated over more than a decade. You'll work across the systems that ingest, process, store, and serve this data reliably: building pipelines, optimizing search, handling PDFs at scale, and exposing clean APIs. Requirements Strong backend engineering background with experience building and operating data-heavy systems in production. Experience deploying and operating services on AWS. Experience designing and maintaining data ingestion pipelines handling messy, heterogeneous sources. Comfortable with web scraping and working with third-party data sources and APIs. Familiarity with Node.js and TypeScript. Itâs fine if you come from a different background, such as Java or Python, but you should be comfortable working in this environment. High standards for data quality. You think carefully about correctness, deduplication, and consistency. Solid understanding of full-text search systems including indexing strategy, relevance tuning, and query optimization. Proficient in building reliable REST APIs. More useful experience Familiarity with academic publishing formats and data sources (PubMed, Crossref, arXivâ¦) Experience with PDF processing pipelines (extraction, transformation, storage and delivery at scale). Experience with LLM-based document processing or ML pipelines for extracting structured data from unstructured text. Large scale web crawling and scraping. Compensation Base compensation â¬60,000ââ¬90,000 based on the level of your experience Bonus/equity program. Please mention the word **NOURISH** and tag RMTQxLjI1My4xMDkuOTc= when applying to show you read the job post completely (#RMTQxLjI1My4xMDkuOTc=). This is a beta feature to avoid spam applicants. Companies can search these words to find applicants that read this and see they're human.
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