EXA
Software Engineer, Knowledge Systems
About this role
Join Exa's Knowledge Systems team to build infrastructure that extracts, connects, and reasons over web-scale information to power next-generation AI search. You'll develop distributed backend systems and data pipelines that enable AI agents to answer factual questions with unprecedented accuracy across billions of facts.
What you'll do
- Design and maintain distributed backend systems processing petabytes of documents and unstructured data
- Build data pipelines for extracting and connecting knowledge from web-scale sources
- Develop systems for entity resolution, data quality validation, and truth discovery
- Optimize systems for correctness, reliability, and performance at massive scale
- Create query languages and retrieval mechanisms for reasoning over billions of facts
- Work on graph traversal and knowledge representation systems
What they're looking for
- Distributed backend systems design and maintenance
- Large-scale data pipeline engineering
- Data quality and reliability optimization
- Graph traversal and entity resolution
- Machine learning systems infrastructure
- Knowledge representation and reasoning
- Database and vector search systems
- Python or similar systems programming language
Benefits
- Premium healthcare coverage (medical, dental, vision)
- Fertility benefits
- 16 weeks fully paid parental leave
- Monthly wellness stipend
- Visa sponsorship available for international candidates
- In-person role in San Francisco
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EXA
Exa builds AI-powered search infrastructure that powers major products and serves hundreds of thousands of developers through APIs, SDKs, and web crawling technology. The company is hiring full-stack engineers, research engineers, distributed systems architects, customer-facing deployment engineers, and growth engineers to expand its platform and customer base.
View all jobs at EXALikely interview questions
- Walk us through a time you built or maintained a distributed backend system at scale. What were the biggest challenges around reliability and correctness?
- Describe your experience building data pipelines. How have you handled processing and validating large volumes of unstructured data, and how did you ensure data quality?