Job Description

About Actively AI

Our thesis is that businesses of the future will be powered by agentic human-in-loop-machines that make every business function 10x more efficient.

Actively AI is building that superintelligent machine for Enterprise GTM organizations, focused on increasing productivity per rep. We power the day-to-day for outbound teams at dozens of companies like Samsara, Ramp, Verkada, and Ironclad.

Why does this matter? Because revenue is the ultimate fuel for businesses. The hundreds of millions of dollars we generate for our customers enables them to employ more people, innovate faster, and deliver more value to their customers.

In addition to top-notch customers that love our product, our team is incredibly high caliber - the co-founders are former Stanford AI researchers and the engineering team comes from Harvard, CMU, Berkeley, Brex, Scale AI, and Google. We're also backed by top investors, including Bain Capital Ventures, First Round Capital (seed investors in Uber, Square, Roblox, Clearbit), Lachy Groom, and Stanford AI faculty.

We have a very ambitious product and scaling roadmap, there’s strong market interest in what we are doing, and it’s time to put the foot on the gas. If you get excited by the thought of working really hard on these kinds of problems with a high caliber team, then Actively AI is the right place for you.

About Actively

Actively is building superintelligence for GTM teams. We train custom reasoning agents to think like a company’s best sales reps, and then use them to evaluate millions of datapoint about every single prospect account in the company’s TAM to feed their sales reps the optimal actions to take every day to maximize revenue.

Our customers include some of the fastest-growing companies out there — Ramp, Verkada, Justworks, Ironclad, etc. — for whom we’ve driven hundreds of millions of dollars in added revenue in the past year.

We’ve raised over $22.5M in funding, including a recent Series A led by Bain Capital Ventures

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 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.