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.

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.

Software Engineer, Knowledge Engine – Query Engine

Responsibilities

Qualification

Required

Benefits

Company

Exa

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