Senior Software Engineer, Data

Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter. Our base salary range is $126,000 - $189,000, and in addition we have generous bonus plans to provide a competitive compensation package.  Who You Are: The Allen Institute for AI (Ai2) is hiring a Senior Data Engineer to build the data infrastructure behind AI research agents that explore and reason over scholarly literature. You'll work on the Semantic Scholar corpus, expanding what it covers and improving the quality of what’s already there, and create the APIs and tooling that these agents rely on at scale. This role sits at the intersection of data engineering and applied ML. You'll own pipelines, design schemas, and ship production services, but you'll also apply practical ML techniques (entity resolution, text classification, embedding-based similarity) to improve data quality and enrich metadata at scale, directly shaping what the agents can do. We're looking for a strong engineer who is comfortable across that full range. Who We Are:  The Agentic Applications team builds open, production-grade systems that power scientific discovery and large-scale AI research. We focus on creating high-quality structured datasets, integrating diverse content types, and enabling downstream applications across search, citation analysis, and model training. The team combines strong engineering practices with close collaboration across Ai2’s product and research orgs to deliver tools and infrastructure used by millions of researchers and developers worldwide. Your Next Challenge: Improve the coverage and quality of the Semantic Scholar corpus across academic papers, patents, and new domain-specific datasets Build and maintain scalable data pipelines for corpus integration, citation resolution, and metadata enrichment Develop and deploy ML models for entity disambiguation, author linking, and topic classification Design and extend APIs that expose structured scholarly data to academic researchers and AI agent workflows Contribute to dashboards and tools for evaluating data quality and model precision Collaborate across engineering and research teams to ensure maintainability, test coverage, and robust deployment What You’ll Need: Required: Bachelor's degree and 8+ years of technical experience; relevant experience may substitute for education. Strong Python engineering skills, especially for building and maintaining data pipelines Experience with SQL and schema design in production settings (PostgreSQL preferred) Familiarity with ML workflows (training classifiers, tuning models, deploying for inference), particularly for large-scale or ambiguous structured datasets Comfortable working with structured data formats (XML/JSON/Parquet) and writing ETL code Experience with workflow orchestration tools (Airflow or similar) and cloud infrastructure (AWS, S3, Docker) Strong communicator and a strong sense of ownership for results Preferred: Experience with author disambiguation, entity resolution, or record linkage problems Experience applying vecto