Constructor Staff Product Manager for Product Discovery Engine responsible for driving business impact through improving search relevance and discovery quality. Leads strategy and roadmap for search quality and ML-driven product discovery.
Responsibilities
Own business impact, not features. Drive measurable lift in search-attributed conversion, revenue per visit, and GMV alongside relevance across our customer base.
Deliver across every retail industry. Move quality and business metrics across all the verticals we serve, accounting for the differences in catalog, language, and shopper intent.
Keep us at the state of the art. Continuously translate the latest ML / IR research and competitive moves into production wins, and ship SOTA ML / LLM-assisted capabilities that beat our previous best on relevance.
Set chapter vision and strategy. Define the multi-quarter direction for the Product Discovery Engine — where we’re heading and why — tie it directly to customer business outcomes, and rally teams behind it.
Lead Search Quality first. Own its strategy and roadmap, driving measurable improvements in the relevance and correctness of results.
Orchestrate across the pipeline. Unify recall, ranking, query understanding, and personalization behind one definition of quality and one set of business metrics, so improvements compound instead of colliding.
Turn ML research into production wins. Partner deeply with ML and data science to take models — from candidate generation and recall to LLM-assisted interpretation — from idea to production, balancing accuracy, latency, and cost.
Make quality measurable and explainable. Build the metrics, evaluation frameworks, and tooling the whole chapter uses to measure, attribute, and explain discovery quality and its business impact.
Own quality under pressure. Lead investigation and triage when issues arise, and design durable, systemic fixes rather than one-off patches.
Grow the mandate. Expand into chapter-wide leadership spanning ML Recall, ML Infra, and — over time — teams like CORE, SABR, and Catalog Management.
What Success Looks Like
Within your first year, you will have:
Qualification
Fluency with retrieval and recallA rigorousComfort operating with ambiguity
Required
Extensive product management experience (typically 8+ years) in search, discovery, ML, or NLP-heavy products, including time operating at a senior or staff level.
A track record of moving business metrics at scale — conversion, revenue, engagement — not just shipping features, and of setting strategy across multiple teams.
Deep understanding of the end-to-end search pipeline: query interpretation → recall / candidate generation → ranking → reranking → personalization → delivery.
Strong ML product depth — owning production models and reasoning about tradeoffs between high-capacity (LLM / batch) and low-latency (real-time) approaches — and a habit of staying current with state-of-the-art research.
Fluency with retrieval and recall, query understanding (tokenization, synonyms, spelling correction, multilingual support), and search evaluation frameworks.
A rigorous, measurement-first mindset that ties every quality investment back to business outcomes.
Excellent cross-functional leadership — able to align teams with different goals behind a shared definition of quality and a shared set of business metrics, working shoulder-to-shoulder with engineering, data science, and ML Ops.
Comfort operating with ambiguity, setting direction where none exists yet, and leading through clarity and credibility rather than the org chart.