Actively AI is a company focused on building superintelligent machines for Enterprise GTM organizations to enhance productivity. The Senior / Staff Software Engineer - Search & Retrieval will design and scale the systems that power AI agents by developing the search, retrieval, and relevance infrastructure essential for optimal data handling and decision-making.
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
Requirements:
- 5+ years building and operating retrieval systems in production, across multiple customers, data sources, or domains
- Background in information retrieval or applied ML
- Experience tuning relevance, deploying reranking strategies, and improving result quality in production
- Experience building retrieval pipelines over fast-changing data, including near-real-time indexing, incremental updates, or event-driven ingestion
- Experience with hybrid retrieval approaches that combine semantic search, keyword and lexical matching, and metadata filtering
- Experience designing or evolving retrieval evaluation frameworks using IR metrics, task-level success signals, or automated quality checks
- Understanding of retrieval architecture holistically, including pre-computing versus retrieving at query time, managing index growth, and designing retrieval paths that stay relevant as scale increases
- Prior experience at a search or retrieval-focused company (Elastic, Algolia, Cohere, Pinecone, Weaviate)
- Experience 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