Software Engineer, Knowledge Engine Query Engine

present of AI; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

present of AI in San Francisco is seeking a Software Engineer for the Knowledge Engine Query Engine to help build fast, reliable search infrastructure for AI models and agents.

You should have strong fundamentals in data structures, algorithms and distributed systems, plus hands-on experience with retrieval systems such as vector, full text or graph databases, and be able to reason about query planning, ranking and latency tradeoffs.

Qualifications

• Strong fundamentals in data structures, algorithms and distributed systems.
• Hands-on experience with retrieval systems such as vector, full text or graph databases.
• Ability to reason about query planning, ranking and performance tradeoffs.

Skills

Data structures

Algorithms

Distributed systems

Production code

Vector search

Full text search

Graph databases

Query planning

Ranking

Performance tuning

Tools

Vector search

Full text search

Graph databases

Embeddings

Job description

Software Engineer, Knowledge Engine Query Engine

A software engineer on a query engine typically needs strong fundamentals in data structures, algorithms and distributed systems, plus comfort writing production code that serves low latency requests at scale. A strong candidate brings hands on experience with retrieval systems such as vector, full text or graph databases, and can reason about query planning, ranking and performance tradeoffs. Since the company builds search for AI models and agents, care for result quality and reliability matters as much as raw speed.

How it fits the AI era

Search infrastructure is becoming core plumbing for AI models and agents, so query engines now serve machine callers that expect fast, precise and well ranked results. AI can help with benchmarking, log analysis and generating test queries, but designing indexes, tuning latency and judging relevance stay human calls. The role sits close to where AI products meet real systems work.

How it builds AI experience

Work here builds practical AI experience through retrieval systems that feed models and agents, including vector search, ranking and evaluation of result quality. A candidate can prepare by building small search or retrieval projects, learning embeddings and hybrid search, and studying how query engines are benchmarked. Familiarity with graph and geospatial data adds depth.

Worth showing

• Vector and full text search
• Query planning and optimization
• Low latency backend code
• Retrieval quality evaluation

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Exa

Exa is an applied AI lab that builds a search engine and API for AI models and agents, per the company.