Senior Product Manager: Recall

Constructor
United Kingdom
Workplace: RemoteFull timeFunction: Product ManagementSkills: ["Empathy","Openness","Curiosity","Continuous improvement","Data-driven decision making"]

Lead two search intelligence teams—Machine Learning & Recall and Query—at the entry point where shopper intent meets candidate retrieval. Own a multi-quarter roadmap and drive improvements in query understanding (tokenization, spell correction, entity extraction, intent classification, LLM-assisted parsing) and recall systems (hybrid keyword + dense embedding + vector retrieval). Measure and deliver lift in conversion, revenue per visit, and search-attributed outcomes while converting SOTA research into low-latency, cost-effective production.

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FursaFursa
Constructor
Constructor
1 week ago

Senior Product Manager: Recall

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Source: Company careers pageValidated by: Fursa AI
Last checked: 8 hours agoStatus: Live

Job Summary

Lead two search intelligence teams—Machine Learning & Recall and Query—at the entry point where shopper intent meets candidate retrieval. Own a multi-quarter roadmap and drive improvements in query understanding (tokenization, spell correction, entity extraction, intent classification, LLM-assisted parsing) and recall systems (hybrid keyword + dense embedding + vector retrieval). Measure and deliver lift in conversion, revenue per visit, and search-attributed outcomes while converting SOTA research into low-latency, cost-effective production.
Location: United Kingdom
Workplace: Remote
Employment Type: Full time
Job Function: Product Management
Seniority: Mid level

Key Responsibilities

  • •Set a unified multi-quarter roadmap across the Query and Machine Learning & Recall teams.
  • •Drive Query intelligence by improving query understanding methods and LLM-assisted parsing across multiple languages.
  • •Advance Recall systems by evolving candidate generation architecture with hybrid keyword and dense embedding approaches.
  • •Own measurable lift in search-attributed conversion, revenue per visit, and GMV across retail customer verticals.
  • •Build evaluation frameworks and orchestrate pipeline handoffs so query context and candidate sets flow cleanly into downstream ranking models.

Pay and Benefits

Perks:Paid LeaveEquityHealth InsuranceRemote WorkHome OfficeLearning BudgetParental Leave

Key Requirements

  • •Highly technical, systems-minded product leadership to unify strategy across Query and Machine Learning & Recall.
  • •Lead query understanding improvements including tokenization, spell correction, entity extraction, intent classification, and LLM-assisted parsing across languages.
  • •Lead recall evolution combining keyword search with dense embeddings, vector retrieval, and hybrid recall models.
  • •Work with ML researchers, data scientists, and software engineers to move SOTA models from research into low-latency, cost-effective production systems.
  • •Use data-backed decision-making and evaluation frameworks to balance accuracy, latency, compute costs, and impact on revenue outcomes.
Experience:Machine learningRetailECommerce
Skills:EmpathyOpennessCuriosityContinuous improvementData-driven decision making
Tech Stack:Vector searchSemantic parsingDense embeddingsLLM-assisted query interpretationTokenizationSpell correctionEntity extractionIntent classificationHybrid recallKeyword searchOnline measurementOffline measurementInference latency

Company Brief

Constructor
Provides an AI-powered site search and product discovery platform for e-commerce and retailers, improving search relevance, recommendations, personalization, and merchandising to boost conversions and average order value.
Industry: Enterprise Software
Company Size: Medium (51 to 250 employees)
Growth: Growth Stage Startup
Headquarters: San Francisco, United States
WebsiteLinkedIn