Senior Staff Software Engineer, Search & Recommendation

New

Cantina LabsSocial AI

Remote (U.S. or Canada)Full-TimeStaff

Salary250,000 - 320,000 USD per year

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Job Details

Bring 10+ years building production software.
Have substantial depth in search, ranking, recommendations, or ML-driven relevance on a consumer-scale product.
Have hands-on experience operating a production search engine such as OpenSearch, Elasticsearch, Lucene, Vespa, or similar, including index and analyzer design and relevance tuning.
Have owned offline evaluation, golden sets, and A/B or interleaving frameworks, and be able to explain metric changes.
Have fluency across candidate generation, feature pipelines, embedding retrieval, and re-ranking.
Understand serving constraints that can prevent offline improvements from succeeding online.
Have strong distributed systems and backend fundamentals.
Have production experience in Go or a comparable systems language.
Be comfortable with the supporting data layer, including SQL warehouses and streaming and batch pipelines.
Have demonstrated ownership of an ambiguous area without a dedicated owner.
Bring clear written communication and judgment when working with Product on relevance, freshness, safety, and latency tradeoffs.
Preferred: experience with AI-generated or rapidly growing content catalogs, severe cold-start conditions, or ranking under trust-and-safety constraints.

Own the architecture and roadmap for search and recommendations, including query understanding, retrieval, ranking, search, discovery, the home feed, and trending and creator leaderboards.
Define relevance quality metrics per surface and build offline evaluation and golden-set tooling.
Run online experiments to assess ranking changes.
Design and evolve ranking systems, including engagement signal pipelines, decay and freshness models, and the combination of pre-computed index-time signals with live query-time re-ranking.
Own OpenSearch in production, including index and mapping design, reindex and cutover safety, query performance, cost, and cluster operational headroom.
Build candidate generation and personalization for recommendation surfaces, partnering with data and ML on feature pipelines and models.
Work with Product and Trust & Safety to define surface quality gates, cold-start behavior, and recommendation exclusions.
Set standards for instrumentation, flag-gated rollouts, and runbooks.
Act as the technical point of contact for discovery across backend teams and mentor engineers whose work touches it.

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