About the RoleWe'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 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 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 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.
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 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.
Nice to Haves- Prior experience at a search or retrieval-focused company (Elastic, Algolia, Cohere, Pinecone, Weaviate) or building shared search infrastructure used across multiple teams or products.
- Experience with entity resolution, knowledge graph construction, or relationship extraction at scale, particularly over noisy or inconsistently structured source data.
Actively AI provides an estimate of the compensation for roles that may be hired as required by state regulations. Compensation may vary based on (a) location, as Actively AI factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate experience. Additionally, Actively AI leverages current market data to determine compensation, so posted compensation figures are subject to change as new market data becomes available. The salary, other compensation, and benefits information is accurate as of the date of this posting. Actively.ai reserves the right to modify this information at any time, subject to applicable law.
Compensation Range:
$160,000-$220,000 USD
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
