Senior Staff Software Engineer, Search & Recommendation

Remote (U.S. or Canada) · USD 250,000-320,000 / year · 10+ years experience · Full-time · Remote · Posted Sep 28, 2026

At a glance

Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning.
If you're excited about the potential AI has to shape human creativity and social interactions, join us in building the future!

Cantina is seeking a Senior Staff Software Engineer to own the end-to-end architecture and roadmap for search and recommendations.
This role involves managing retrieval, ranking, and relevance quality for a production system serving live traffic.
As the first dedicated hire for this area, the engineer will set the technical direction for search, discovery, and feed surfaces.

Own the architecture and roadmap for search and recommendations including query understanding, retrieval, and ranking.
Define quality metrics per surface and build offline evaluation and golden-set tooling.
Run online experiments to validate ranking changes.
Design and evolve the ranking stack, including engagement signal pipelines and freshness models.
Manage OpenSearch in production, including index design, query performance, and cluster operations.
Build candidate generation and personalization for recommendation surfaces.
Partner with data and ML teams on feature pipelines and models.
Collaborate with Product and Trust & Safety teams to define quality gates and content moderation standards.
Set engineering standards for instrumentation, flag-gated rollouts, and operational runbooks.
Act as the technical point of contact for discovery and mentor other engineers.

10+ years of experience building production software with depth in search, ranking, recommendations, or ML-driven relevance.
Hands-on expertise with production search engines like OpenSearch, Elasticsearch, Lucene, or Vespa.
Experience owning offline evaluation, golden sets, and A/B or interleaving frameworks.
Fluency in the recommender stack including candidate generation, feature pipelines, and embedding retrieval.
Strong distributed systems and backend fundamentals.
Production experience in Go or a comparable systems language.
Experience with SQL warehouses and streaming/batch pipelines.
Demonstrated ownership of ambiguous areas with no dedicated owner.
Clear written communication skills.
Ability to balance tradeoffs between relevance, freshness, safety, and latency.