Senior/Staff Software Engineer (Search & Retrieval)
AI summary of the role
Senior/Staff Software Engineer to build and scale the search, retrieval, and relevance infrastructure powering Actively's AI agents.
No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.
What you’ll do
- Design and scale search and retrieval infrastructure covering indexing, querying, ranking, and filtering across diverse customer data sources.
- Design enrichment and entity extraction systems to pull structure from call transcripts, documents, and signals.
- Define data representation and storage choices including granularity, embedding models, and index configuration.
- Build and deploy reranking layers to maximize relevance for agent queries.
What you’ll bring
- 5+ years building and operating retrieval systems in production across multiple customers or domains.
- Background in information retrieval or applied ML with production relevance tuning and reranking experience.
- Experience with retrieval pipelines over fast-changing data including near-real-time indexing or event-driven ingestion.
- Experience with hybrid retrieval approaches combining semantic search, keyword/lexical matching, and metadata filtering.
Technologies
search · retrieval · ranking · reranking · indexing · embedding models · semantic search · keyword matching · metadata filtering · LLM evaluation
About Actively AI
SaaS platform deploying per-account AI agents that research accounts and surface revenue opportunities 24/7 for enterprise sales teams.
Series B · 50–200 people
Source and classification
Internal deployment & tooling · Evidence for this classification:
Senior / Staff Software Engineer - Search & Retrieval to build and scale the systems that power Actively’s AI agents to find, rank, and reason over data. When an Actively agent decides which account to prioritize or what action to take next, it reasons over retrieved context; data pulled from customer records, call transcripts, signals, and internal intelligence. Get that retrieval right and the agent acts with precision. Get it wrong and it doesn't matter how good the underlying model is. You'll design and build the search, retrieval, and relevance infrastructure that feeds every agent at Actively from the enrichment and entity extraction that turns raw data into something queryable, to the ranking systems that determine what context an agent actually sees. The data is diverse, messy, and customer-specific. Freshness matters. So does precision. And the consumer isn't a human browsing
More from the job description
About Actively AI Actively AI is defining a new category: Intelligence-Led Revenue. Revenue organizations have always been bottlenecked on human capacity. Reps triage which accounts get attention. Context disappears at every handoff. On any given day, the vast majority of accounts have exactly zero people thinking about them. Actively addresses this at the structural level. Our platform deploys Per-Account AgentsTM across our customers’ TAM, working 24/7 to research, identify opportunities, and advance next steps without being asked. Leading enterprises including Ramp, Ironclad, and Samsara are already making this shift. Our co-founders are former Stanford AI researchers, and the team comes from Harvard, CMU, Berkeley, Brex, Scale AI, and Google. We've raised $68M from TCV, First Harmonic, Bain Capital Ventures, First Round Capital, and more. About the Role We’re looking for a Senior / Staff Software Engineer - Search & Retrieval to build and scale the systems that power Actively’s AI agents to find, rank, and reason over data. When an Actively agent decides which account to prioritize or what action to take next, it reasons over retrieved context; data pulled from customer records, call transcripts, signals, and internal intelligence. Get that retrieval right and the agent acts with precision. Get it wrong and it doesn't matter how good the underlying model is. You'll [... source excerpt omitted ...] ment and entity extraction that turns raw data into something queryable, to the ranking systems that determine what context an agent actually sees. The data is diverse, messy, and customer-specific. Freshness matters. So does precision. And the consumer isn't a human browsing results but it's a model that will act on whatever you give it. What You’ll Do 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 [... source excerpt omitted ...] catching regressions before they affect agent behavior. Who You Are Deep experience in search or retrieval systems. You have 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 affe
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