Senior Staff Software Engineer, Search & Recommendation

Cantina Labs is a social AI company developing real-time models and an AI character platform. The company is seeking a Senior Staff Software Engineer to own search and recommendation architecture, retrieval, ranking, relevance measurement, personalization, and the roadmap for discovery surfaces. The role will also establish engineering standards, operate OpenSearch in production, collaborate with Product, Data, ML, and Trust & Safety, and mentor engineers.

Responsibilities:

• Own the architecture and roadmap for search and recommendations end to end — query understanding, retrieval, ranking, and the surfaces they feed: search, discovery, the home feed, and our trending and creator leaderboards
• Make relevance a measured discipline rather than an opinion: define the quality metrics per surface, build the offline evaluation and golden-set tooling, and run the online experiments that decide whether a ranking change ships
• Design and evolve the ranking stack — engagement signal pipelines, decay and freshness models, and the pattern we use to combine pre-computed index-time signals with live re-ranking at query time
• Own OpenSearch in production: index and mapping design, reindex and cutover safety, query performance, cost, and the operational headroom of the clusters behind every discovery surface
• Build candidate generation and personalization for recommendation surfaces, and partner with data and ML on the feature pipelines and models behind them
• Work directly with Product and Trust & Safety on what “good” means for each surface — quality gates, cold-start behavior for brand-new creators and characters, and what must never be recommended
• Set the engineering standard for the area: instrumentation, flag-gated rollouts, and runbooks that let other teams change ranking behavior without breaking it
• Act as the technical point of contact for discovery across backend teams, and mentor the engineers whose work touches it

Requirements:

• 10+ years building production software, with substantial depth in search, ranking, recommendations, or ML-driven relevance on a consumer-scale product
• Hands-on expertise with a production search engine — OpenSearch, Elasticsearch, Lucene, Vespa, or similar — covering index and analyzer design, relevance tuning, and operating the cluster, not only querying it
• A rigorous approach to relevance measurement: you have owned offline evaluation, golden sets, and A/B or interleaving frameworks, and you can explain what moved a metric and why
• Fluency across the recommender stack — candidate generation, feature pipelines, embedding retrieval, re-ranking — and a working understanding of the serving constraints that make an offline win fail online
• Strong distributed systems and backend fundamentals, with production experience in Go or a comparable systems language, and comfort in the data layer behind it (SQL warehouses, streaming and batch pipelines)
• Demonstrated ownership of an ambiguous area: you have picked up something with no dedicated owner, decided what mattered, and can show the result
• Clear written communication and the judgment to work with Product on real tradeoffs between relevance, freshness, safety, and latency
• experience with AI-generated or rapidly growing content catalogs, severe cold-start conditions, or ranking under trust-and-safety constraints