Senior Product Manager: Recall

We're looking for a highly technical, systems-minded Senior Product Manager to lead two critical search intelligence teams: Machine Learning & Recall and Query. Search at Constructor is fundamentally an ML challenge, and this role sits at the absolute entry point of our discovery pipeline, right where shopper intent meets candidate retrieval.

In this role, you will bridge the Query team (understanding what a shopper means across intent, entities, and language) with the Machine Learning & Recall team (retrieving the exact right set of candidate products across massive catalogs). You will define strategy across both domains, balancing state-of-the-art techniques, such as vector search, semantic parsing, dense embeddings, and LLM-assisted query interpretation, with low-latency production execution. You won't be measured on shipping features for the sake of it; you'll be measured on candidate quality, query accuracy, and driving business outcomes like conversion rate, search-attributed revenue, and revenue per visit.

• Set a unified roadmap: Define the multi-quarter vision and strategy across both the Machine Learning & Recall and Query teams, ensuring query understanding and candidate generation evolve hand-in-hand.

• Drive Query intelligence: Lead the Query team to push boundaries in tokenization, spell correction, entity extraction, intent classification, and LLM-assisted query parsing across multiple languages.

• Advance Recall systems: Lead the Machine Learning & Recall team in evolving candidate generation architecture, seamlessly combining traditional keyword search with modern dense embeddings, vector retrieval, and hybrid recall models.

• Move core business metrics: Own measurable lift in search-attributed conversion, revenue per visit, and GMV across all retail customer verticals.

• Turn research into production wins: Work shoulder-to-shoulder with ML researchers, data scientists, and software engineers to take SOTA models from research to low-latency, cost-effective production systems.

• Balance ML performance trade-offs: Make data-backed decisions balancing model complexity, accuracy, inference latency, compute costs, and real-time execution constraints.

• Build evaluation frameworks: Establish robust offline and online measurement systems to evaluate retrieval precision and query understanding accuracy against customer revenue outcomes.

• Orchestrate pipeline handoffs: Ensure candidate product sets and query context flow cleanly into downstream ranking and search quality models without signal loss.

• Solve systemic issues: Lead technical triage when query interpretation or retrieval anomalies occur, building durable, automated fixes rather than one-off patches.

This position is fully remote - Constructor.io is a remote-first company.

Within your first year, you will have:

• Evolved our hybrid recall framework to measurably improve candidate generation precision for long-tail queries and complex catalog structures.

• Driven quantifiable lift in search-attributed conversion and revenue per visit across major customer verticals.

• Stood up an integrated evaluation framework connecting query parsing and candidate recall directly to business outcomes.

• Optimized inference latency and compute infrastructure to ensure high-capacity ML models run fast and cost-effectively in production.