Senior Software Engineer, Data and Integrations

New

ElicitAI research software

Oakland, CA (or remote within US timezones), time zone overlap (between GMT and GMT-8)Full-TimeSenior

SalarySenior (L4): $185-270k + equity; Expert (L5): $215-305k + equity; Principal (L6): >$260 + significant equity

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Job Details

Have 5+ years of software engineering experience.
Have owned production systems and made architecture decisions.
Bring strong Python skills; the role uses Python daily and interviews include live coding.
Have working knowledge of TypeScript; React experience is a plus.
Have experience designing, debugging, and tuning distributed data pipelines; Spark experience is especially useful.
Have solid SQL skills and practical knowledge of partitioning, joins, columnar formats, and data layout effects on performance and cost.
Have integrated external data from APIs, bulk exports, and file drops, including handling changing schemas, rate limits, partial failures, and overlapping records.
Care about data quality, entity resolution, provenance, and observability, and have a plan for failure scenarios.
Have shipped user-facing features end to end.
Demonstrate product judgment and connect infrastructure work to user needs.
Have at least one year of experience at an early- or growth-stage company.

Own the coverage, freshness, quality, reliability, and cost of Elicit’s scholarly search and data platform.
Turn customer and business needs into technical plans, working with product, sales, and partners.
Bring new kinds of evidence into Elicit and make them useful to LLM-based features while preserving structure, citations, and provenance.
Build secure, rights-aware integrations for enterprise customers and content partners.
Translate access, privacy, isolation, and auditability requirements into product behavior.
Ship features from brief to production across the frontend, backend, and data layers.
Release in small slices with staged rollouts, monitoring, and rollback plans.
Operate shipped systems, observe product usage, and manage integration complexity.
Build product features beyond integrations and make the data platform usable by other engineers.
Use coding agents in day-to-day work and carefully review their output.

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