Staff Product Manager: Product Discovery Engine
Job description
A Staff Product Manager role at Constructor owning the strategy and roadmap for the Product Discovery Engine, starting with the Search Quality team and growing to chapter-wide leadership. The role is measured on relevance and business outcomes such as conversion, revenue per visit, and search-attributed GMV.
At a glance
- Work mode
- Remote
What you'll do
- 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, moving quality and business metrics across all verticals served, accounting for differences in catalog, language, and shopper intent
- Keep us at the state of the art by translating the latest ML / IR research and competitive moves into production wins and shipping SOTA ML / LLM-assisted capabilities
- Set chapter vision and strategy, defining the multi-quarter direction for the Product Discovery Engine and tying it to customer business outcomes
- Lead Search Quality first, owning its strategy and roadmap and driving measurable improvements in relevance and correctness of results
- Orchestrate across the pipeline, unifying recall, ranking, query understanding, and personalization behind one definition of quality and one set of business metrics
- Turn ML research into production wins, partnering with ML and data science to take models from idea to production while balancing accuracy, latency, and cost
- Make quality measurable and explainable by building metrics, evaluation frameworks, and tooling
- Own quality under pressure, leading investigation and triage and designing durable, systemic fixes
- Grow the mandate into chapter-wide leadership spanning ML Recall, ML Infra, and over time teams like CORE, SABR, and Catalog Management
What we're looking for
- 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
Benefits
- Unlimited vacation time — we strongly encourage all of our employees to take at least 3 weeks per year
- A competitive
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About Constructor
Powers product search and discovery for some of the largest retailers in the world
Constructor powers product search and discovery for some of the largest retailers in the world, serving billions of requests every week. It differentiates itself by focusing on metrics over features and reinventing search and discovery from the ground up as a machine learning challenge with the specific goal of improving metrics like revenue. The company is approximately doubling year over year despite the market slowing down and has customers in every eCommerce vertical.
Key facts
- Product search and discovery / eCommerce technology
- Serves billions of requests every week
- Approximately doubling year over year despite the market slowing down
- Customers in every eCommerce vertical
- Reinvents search and discovery as a machine learning challenge focused on metrics like revenue
What they value
- Empathy
- Openness
- Curiosity
- Continuous improvement
- Metrics that matter
- Empowering everyone in a company to do what they think is best