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Exa
2 months ago

Software Engineer (Distributed Data Systems) Exa

Search infrastructure for AI to filter internet knowledge for complex queries.

Permanent
San Francisco
Remote: Not specified
150K to 300K $ a year
> 5 years
2 months ago

You'll be part of

116 employees
Artificial Intelligence / Machine Learning

Our take

The way we currently query AIs means that the models we rely on are themselves reliant on the quality of internet search tools. To answer this, Exa is building a tool that allows AI models to perform the equivalent of a web search, making the models more effective in their responses to our questions. Exa’s embeddings-based search engine is free of “hallucinations” (those bizarre search query answers that AI sometimes brings back to users). Also, in the same way that LLMs complete sentences, its predictive technology does the same for search queries by providing the next probable link. It's an alluring product for companies and individuals who want to accelerate their research answers. It's also one of a kind at present, which has led to several successful funding rounds which will help accelerate its product. If it maintains its current momentum, Exa could well be the AI search engine of the future.

You'll be working on

  • As a Data Engineer, you’ll architect and build the data infrastructure that powers everything we do—from crawling billions of pages to training our embedding models to serving real-time search
  • You’ll have enormous autonomy in designing systems that scale to hundreds of petabytes
  • Design a lakehouse architecture that handles 100+ PB of web crawl data
  • Build streaming pipelines that process billions of documents per day for real-time indexing
  • Architect the data layer for our embedding training infrastructure on Ray
  • Scale our ClickHouse deployment to handle analytical queries across petabytes of search logs

We're looking for

Data engineering
Clickhouse
Kafka
  • If you’ve ever wanted to build data pipelines at a scale that most companies only dream about, this is your chance
  • An obsessive focus on reliability and building systems that don’t page you at 3am
  • Familiarity with Ray, Spark, or ClickHouse at production scale
  • Deep understanding of lakehouse architectures (Delta Lake, Iceberg, Hudi) and when to use them
  • Hands-on experience with streaming data systems (Kafka, Flink, or similar)
  • Experience building and operating large-scale distributed data processing pipelines
  • Experience with Lance or other vector-native storage formats
  • Background in GPU-accelerated data processing (RAPIDS, cuDF)

The workplace

San Francisco, California, United States
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