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Senior/Staff Software Engineer (Search & Retrieval)

Actively AI· Engineering
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San Francisco, CA$160k - $220kPosted 6mo ago

About the role

Senior/Staff Software Engineer focused on building and scaling the search, retrieval, and relevance infrastructure to power Actively AI agents. Responsibilities include designing retrieval layers, turning unstructured data into queryable form, ranking systems, shared retrieval primitives, and end-to-end retrieval quality evaluation.

Responsibilities

  • Build the retrieval layer agents depend on. Design and scale the search and retrieval infrastructure that feeds Actively's agents, covering indexing, querying, ranking, and filtering across diverse customer data sources.
  • Turn raw, unstructured data into something retrievable. Design enrichment and entity extraction systems that pull structure, relationships, and context out of call transcripts, documents, and signals, making them queryable in ways that improve what agents actually see.
  • Own the Search for Agents Architecture: Define how data gets represented and stored, making deliberate choices about granularity, embedding models, and index configuration for different data types and use cases.
  • Build and iterate on ranking systems. Design and deploy reranking layers that maximize relevance for agent queries, and evolve them as data patterns and use cases change.
  • Develop shared retrieval primitives. Build the APIs and retrieval interfaces used by the Intelligence, Assistant, and Orchestration teams, balancing flexibility with consistency across consumers.
  • Own retrieval quality end to end. Build and maintain evaluation infrastructure using classical IR metrics, task-level success signals, and LLM-based techniques, catching regressions before they affect agent behavior.

Qualifications

  • Deep experience in search or retrieval systems. 5+ years building and operating retrieval systems in production, across multiple customers, data sources, or domains, and understand what relevance actually means at scale.
  • Background in information retrieval or applied ML. You've tuned relevance, deployed reranking strategies, and improved result quality in production, not just in experiments.
  • Understands the freshness problem. You've built retrieval pipelines over fast-changing data, including near-real-time indexing, incremental updates, or event-driven ingestion, and know how freshness trade-offs affect system design.
  • Comfortable with hybrid retrieval approaches. You've worked with systems that combine semantic search, keyword and lexical matching, and metadata filtering to balance recall, precision, and reliability.
  • Rigorous about evaluation. You've designed or evolved retrieval evaluation frameworks using IR metrics, task-level success signals, or automated quality checks, and you treat regressions as real incidents.
  • Thinks about retrieval architecture holistically. You know when to pre-compute versus retrieve at query time, how to manage index growth, and how to design retrieval paths that stay relevant as scale increases.

Skills

Required:Search or retrieval systemsInformation retrieval / applied MLFreshness-aware retrievalHybrid retrieval

Benefits

  • Competitive Early-Stage Equity
  • Health, Dental, Vision Coverage
  • Unlimited PTO + Recharge Days
  • Catered Lunch on Tuesday & Friday, Dinners every day
  • Fully Stocked Kitchen
  • Cutting-Edge Tech & Tools
  • Annual Off-sites & Monthly Events
  • Commuter Benefits
  • Cozy Office in NYC
Last updated Jul 15, 2026. Interested? Apply on the company site.

About Actively AI

GTM Superintelligence to Maximize Revenue

Actively AI is GTM superintelligence built to boost revenue per rep. The world’s most innovative revenue teams use our per-account agents to power next-best actions across their entire TAM and scale AI-driven growth.

GTM superintelligenceAI-driven growthper-account agentsenterprise-scale GTM
Company profileWebsite

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