Exa - Software Engineer, Knowledge Engine – Query Engine
Requirements
• 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. • Location: • 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. • Visas: • 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.
Responsibilities
• 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. • Own storage and indexing across backends: • 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. • Decide how best to search: • 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. • Make it fast at scale: • 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. • Keep answers consistent while the data converges: • 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. • Measure precision and completeness:
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