---
title: 'Software Engineer, Knowledge Engine – Query Engine at Exa'
canonical: 'https://feeny.ai/job/software-engineer-knowledge-engine-query-engine-exa-zurich-878qt7c4g1n8'
type: 'job'
last_seen: '2026-11-07'
---

# Software Engineer, Knowledge Engine – Query Engine at Exa

- **Company:** Exa
- **Location:** Zurich, Switzerland
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-29
- **Last confirmed live:** 2026-11-07
- **Apply:** https://jobs.ashbyhq.com/exa/352ee095-81eb-40b8-99e3-7355317aa208

## Job description

Exa is an applied AI lab building a search engine unlike the world has ever seen. We build massive-scale infra to crawl the entire web, train state-of-the-art embedding models to process it, and design super high performant vector databases to retrieve over it. We now power search for Cursor, Cognition, HubSpot, and over 400,000 developers and have raised $350m from Lightspeed, Benchmark, and a16z. Our ultimate goal is to build perfect search over all the world's information, far beyond Google. If you want to build massive-scale ML systems that will define the way the new AI world consumes information, this is the place for you. The Knowledge Platform team builds Exa's knowledge engine. We continuously synthesize information from across the web and beyond into one of the world's largest open-domain knowledge graphs, with hundreds of billions of evidence-backed claims. The work has two halves: construction pipelines that decide what to trust, and retrieval engines that decide how best to search it. Our goal is to give agents answers to any factual question about the world with unprecedented precision and completeness. In this role, you'll own the query engine that searches hundreds of billions of claims across a variety of retrieval backends, from a distributed graph database to vector, full-text, time-series and geospatial indexes. This is greenfield work with a massive scope. No off-the-shelf system answers precise questions over hundreds of billions of evidence-backed claims at agent speed, so you'll make foundational decisions, from storage layout and indexing across engines to the query language and the planner that routes work between them, that every query depends on.

## What you'll do

- Own storage and indexing across backends: data layout, partitioning and indexes for graph, vector, full-text, time-series and geospatial retrieval, with bulk snapshot builds and live ingest that never take the system down.
- Decide how best to search: cost-based planning from cardinality estimates that chooses, for each question, which backends to use, in what order and how to join their results, executed as traversals, retrievals, filters and aggregations in one plan.
- Make it fast at scale: serve thousands of queries per second, profiling and cutting p50/p99 latency, memory and cost per query under high concurrent load, from on-disk layout to network fan-out.
- Keep answers consistent while the data converges: the graph is fed by live updates and bulk snapshots at once and is eventually consistent by design, so you'll own what a query may see while it converges: consistent snapshot promotion, ordered application of live updates and stable pagination across versions.
- Measure precision and completeness: end-to-end checks every deployment must pass, plus evals and metrics that show when a plan returns wrong results or misses ones it should have found.

## What we're looking for

- You've worked deep inside a distributed database, query engine, search engine or storage system.
- Strong grasp of indexing, query planning and optimization, and storage formats.
- Solid grasp of distributed systems fundamentals: consensus, replication, sharding and fault tolerance.
- Strong in a systems language (Go, Rust or C++).
- You profile before you optimize, and you've shipped measured latency wins in production.
- You obsess over correctness, reliability and data quality as much as scale.

Bonus

- Graph databases, or combining vector, full-text, time-series and geospatial retrieval
- Compilers, query language design or cost-based optimizers

Example projects

- Build a system that retrieves and reasons over everything known about any company or person in the world, answering any factual question precisely and completely.
- Answer precise multi-hop questions over hundreds of billions of claims at thousands of QPS with interactive latency.
- Build a planner that picks the right mix of graph, vector, full-text and geospatial retrieval for any question, and proves it chose well.
- Make new knowledge queryable seconds after it's synthesized, without ever serving an inconsistent result.

Logistics

- Location: This is an in-person opportunity in Zurich.
- Visas: We're happy to sponsor international candidates (e.g., STEM OPT, OPT, H1B, O1, E3). While we cannot guarantee your visa, we have historically been successful in sponsoring candidates from all over the world. If you receive an offer, our team will work hard to get you a visa.
- Benefits: We offer premium healthcare benefits (medical, dental, vision), fertility benefits, 16 weeks of fully paid parental leave for all new parents, and a monthly wellness stipend to all of our employees.

Exa is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, age, veteran status, marital status, pregnancy or related conditions, criminal histories consistent with applicable law, or any other basis protected by applicable law. Exa is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, age, veteran status, marital status, pregnancy or related conditions, criminal histories consistent with applicable law, or any other basis protected by applicable law.

## About Exa

## Company Overview
- **One-liner**: Exa builds a web-scale search API and deep research engine purpose-built for AI agents and developers, organizing the web for the agentic era.
- **Entity Type**: Private (Series B)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2021
- **Founders**: Will Bryk (CEO) and Jeff Wang (Co-founder)

## Core Business
- **Primary industry/industries**: AI search infrastructure, applied AI research, developer tools
- **Target customers**: B2B – developers, AI agent companies, enterprise teams (e.g., Cursor, Cognition, HubSpot, OpenRouter, Monday.com)
- **Mission or purpose statement**: “Organize all knowledge” and “reinvent how the world gets information” – building a search engine for the AI era that exceeds the quality and latency of traditional search.

## Products & Services
- **[Exa Search API](https://exa.ai/)**: A high‑throughput web search API delivering results in under 180ms, with support for low‑latency instant search, deep research, crawling, and structured output extraction. Includes “Highlights” (token‑efficient excerpts), company & people search, and code search. SOC 2 Type II certified, zero data retention option, single sign‑on.
- **Exa Embeddings & Neural Retrieval**: Proprietary state‑of‑the‑art embedding models trained on Exa’s own cluster of hundreds of H200s, achieving best‑in‑class accuracy on FRAMES, Tip‑of‑Tongue, and Seal0 benchmarks.
- **Custom AI Search Solution**: Enterprise‑grade search infrastructure that can be tailored for specific verticals and integrated via a single API.

## Market Standing
- **Valuation/Market Cap**: $2.2 billion (as of May 2026) – reported by Bloomberg and referenced on Exa’s about page.
- **Key Metric**: Total funding $111.4 million (Pre‑Seed, Seed, Series A, Series B)
- **Notable Investors/Partners**:
  - **Investors**: Lightspeed Venture Partners (led Series A), Benchmark (led Series B), Nvidia, Y Combinator, and individual advisors such as Tal Broda (former VP of Compute, OpenAI) and researchers from Google/Bing.
  - **Customers & Integrations**: Cursor, Cognition (Devin), HubSpot, OpenRouter, Monday.com, and over 400,000 developers.
- **Growth Signals**:
  - Headcount: 75 employees (+127.7% YoY), with active hiring across 17 open roles.
  - Revenue: Not publicly disclosed, but traction with marquee customers and rapid valuation growth from ~$22M (Seed) to $2.2B in under two years.
  - Team distribution across 10 countries, with a strong engineering and research core (26% technical, 5% research).

## Competitive Advantages
- **Web‑scale infrastructure**: Crawls and indexes 500B+ webpages with a proprietary neural retrieval stack that improves predictably with compute.
- **Latency & accuracy leadership**: Fastest search API (<180ms) and top scores on industry benchmarks (FRAMES, Tip‑of‑Tongue, Seal0).
- **Agent‑native design**: Built specifically for the workflow of LLM agents – token‑efficient highlights, structured outputs, and low‑latency to deep research in a single API.
- **Enterprise trust**: SOC 2 Type II, zero data retention, SSO, and privacy controls.

## Strategic Focus
- **Current priorities**: Expand the search infrastructure for the coming “1000x more searches from AI agents than Google today.” Continue investing in end‑to‑end neural architectures, scale the cluster, and deepen integrations with leading AI platforms.
- **Direction**: Become the default “knowledge layer” for all AI agents, reinventing search from a link‑based model to a high‑quality, structured answer engine.

## Why Work Here
- **Culture**: Applied AI research lab with a mission to build the world’s best search engine. In‑person team (San Francisco headquarters) with daily team meals, weekly milk tea, monthly picnics, and regular celebrations.
- **Remote/hybrid policy**: Fully in‑person for most roles, with flexibility for certain positions and visa‑relocation support.
- **Benefits**: Premium medical/dental/vision, monthly wellness and commuter benefit, fertility/family planning reimbursement (up to $15k lifetime), 16 weeks paid parental leave, daily lunch and dinner, snacks, and fresh bread from a local bakery.
- **Engineering culture**: Emphasis on research‑driven engineering – working on massive‑scale indexes, novel retrieval systems, and training state‑of‑the‑art models on Exa’s own H200 cluster.

## Sources
1. [Exa About Page](https://exa.ai/about)
2. [Exa Careers Page](https://exa.ai/careers)
3. [Exa Product Page](https://exa.ai/)
4. [Exa LinkedIn](https://www.linkedin.com/company/exa-ai)
5. [Y Combinator Company Profile](https://www.ycombinator.com/companies/exa)

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