Software Engineer, Knowledge Engine – Query Engine

pExa 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. /ppOur 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. /ppThe 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. /ppIn this role, you'll own the bquery engine /b 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. /ppThis 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. /ph3What you'll do /h3ullipbOwn storage and indexing across backends: /b 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. /p /lilipbDecide how best to search: /b 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. /p /lilipbMake it fast at scale: /b 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. /p /lilipbKeep answers consistent while the data converges: /b 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. /p /lilipbMeasure precision and completeness: /b 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. /p /li /ulh3What we're looking for /h3ullipYou've worked deep inside a distributed database, query engine, search engine or storage system. /p /lilipStrong grasp of indexing, query planning and optimization, and storage formats. /p /lilipSolid grasp of distributed systems fundamentals: consensus, replication, sharding and fault tolerance. /p /lilipStrong in a systems language (Go, Rust or C++). /p /lilipYou profile before you optimize, and you've shipped measured latency wins in production. /p /lilipYou obsess over correctness, reliability and data quality as much as scale. /p /li /ulh3Bonus /h3ullipGraph databases, or combining vector, full-text, time-series and geospatial retrieval /p /lilipCompilers, query language design or cost-based optimizers /p /li /ulh3Example projects /h3ullipBuild a system that retrieves and reasons over everything known about any company or person in the world, answering any factual question precisely and completely. /p /lilipAnswer precise multi-hop questions over hundreds of billions of claims at thousands of QPS with interactive latency. /p /lilipBuild a planner that picks the right mix of graph, vector, full-text and geospatial retrieval for any question, and proves it chose well. /p /lilipMake new knowledge queryable seconds after it's synthesized, without ever serving an inconsistent result. /p /li /ulh3Logistics /h3ullipbLocation: /b This is an in-person opportunity in Zurich. /p /lilipbVisas: /b 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. /p /lilipbBenefits: /b 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. /p /li /ulpExa 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. /ppExa 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. /p #J-18808-Ljbffr