Software Engineer, Knowledge Engine, Data Infrastructure
Exa has an opening for a Software Engineer, Knowledge Engine, Data Infrastructure in Zurich, Switzerland. The listing describes it as a full-time position.
DGJ summary of this job
Our own short read of this listing, built only from the facts it gives. The employer’s original text follows further down.
Exa has an opening for a Software Engineer, Knowledge Engine, Data Infrastructure in Zurich, Switzerland. The listing describes it as a full-time position.
The advert mentions Python and Spark among the skills and tools for this role.
The employer does not state a salary in this listing; DGJ Insight below shows an official pay benchmark for the place where one exists.
Benefits mentioned in the advert: bonus.
Visa sponsorship is not mentioned. Applicants from outside Switzerland should check the official work permit route before applying.
Dev Global Jobs found this job on Arbeitnow on Nov 10, 2026.
- Employer
- Exa
- Location
- Zurich, Switzerland
- Job type
- Full-time
- Pay
- Not stated by the employer
- Found
- Nov 10, 2026 on Arbeitnow
- Employer text
- 607 words, about 3 min to read
- Apply only on the official page (arbeitnow.ch). Dev Global Jobs is not the employer and never asks for a fee.
- Mirror the skills the advert names (Python and Spark) in your CV where you really have them; applicant tracking systems look for these words.
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DGJ Insight
Our own data about this role and place, from official statistics and our live job index.
Source: OECD Average annual wages, 2025
UN living-cost index for Geneva (ICSC post adjustment 80.3), as of 1 Oct 2026.
Source: ICSC post adjustment classifications (icsc.un.org)
- Work permit B / L (non-EU quota): Highly qualified specialists, employer proves no Swiss or EU candidate; annual quotas
- EU/EFTA free movement permits: Registration for EU/EFTA nationals with a job
About the role
Full text from the employer, as published on Arbeitnow, arranged into sections. Nothing added or removed.
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 build the knowledge construction pipelines: turning everything from a 100k docs/s web crawl to vendor datasets into a graph of what is true about the world, and keeping it correct as data changes.
This is greenfield work with a massive scope. There is no off-the-shelf system for building a model of the world from the open web, so you'll make foundational decisions, from how claims are represented and resolved to how trust is assigned and how the graph evolves over time, that everything built on top of it depends on.
01What you'll do
Continuously synthesize the live web crawl, vendor deliveries and APIs, and other off-the-web sources into one typed graph: extraction that turns pages and records into ontology-shaped claims, each carrying its provenance, timestamp and confidence.
Bring every new source in cleanly: versioned, append-only history with schema evolution, late-arriving and corrected records, and replayable cursors, so every claim traces back to the exact delivery that produced it.
Decide what to trust: entity resolution across billions of records (blocking, pairwise scoring, clustering) into canonical people, companies, places and publications, then truth discovery that weighs source reliability, recency and corroboration to reconcile conflicting claims.
Keep that picture current and convergent: streams arrive continuously, out of order and at very different rates, so the graph is eventually consistent by design. You'll build incremental computation over change data feeds, where each update is a diff of what changed and every job is idempotent and resumable, so the graph converges to the same state however runs interleave, fail or replay, and readers only ever see committed, consistent versions.
Measure precision and completeness: labeled evals for extraction and resolution, per-source coverage and correctness metrics, and lineage that pinpoints which source or stage broke and why.
02What we're looking for
You've built and operated large-scale distributed data pipelines in production over petabytes of structured and unstructured data, with Spark over a lakehouse format (Delta Lake, Iceberg, Hudi).
You understand incremental processing deeply: change data capture, idempotent writes, backfills and reprocessing.
Strong in Python and at least one systems language; you can read and tune Spark query plans.
You obsess over correctness, reliability and data quality as much as scale.
03Bonus
Entity resolution, record linkage, knowledge graphs or ontologies
Information extraction or truth discovery at scale
Lance or other columnar/vector-native formats
04Example projects
Take a new billion-row dataset from first delivery to queryable entities in days, not months.
Keep a graph of hundreds of billions of claims current with incremental updates, so a fact that changes on the web reaches it in minutes.
Build a system that knows every company and person in the world, with one evidence-backed record each, resolved across the web and every source we ingest.
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.
Compensation: $180K, $350K • Offers Equity
- • $180K, $350K • Offers Equity
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