Staff Product Manager: Product Discovery Engine

• 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.

• 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.

• Unlimited vacation time — we strongly encourage all of our employees to take at least 3 weeks per year. • A competitive compensation package including stock options. • Company-sponsored US health coverage (100% paid for employee). • Fully remote team — choose where you live. • Work-from-home stipend! We want you to have the resources you need to set up your home office. • Apple laptops provided for new employees. • Training and development budget for every employee, refreshed each year. • Parental leave for qualified employees. • Work with smart people who will help you grow and make a meaningful impact. This position is fully remote — Constructor.io is a remote-first company. Diversity, Equity, and Inclusion at Constructor At Constructor.io we are committed to cultivating a work environment that is diverse, equitable, and inclusive. As an equal opportunity employer, we welcome individuals of all backgrounds and provide equal opportunities to all applicants regardless of their education, diversity of opinion, race, color, religion, gender, gender expression, sexual orientation, national origin, genetics, disability, age, veteran status or affiliation in any other protected group. How we handle your data. By submitting this application, you acknowledge that Constructor will collect and process the personal data you provide for the purpose of evaluating your candidacy for this role. If you are not selected, we will retain your data for a limited period as described in our Recruitment Privacy Notice. After that period your data will be deleted or anonymized. Find Jobs in Germany on Arbeitnow

Staff Product Manager, Product Discovery Engine About Us Constructor powers product search and discovery for some of the largest retailers in the world. We serve billions of requests every week, and you’ve probably seen our results somewhere and used our product without knowing it. We differentiate ourselves 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. We’re approximately doubling year over year despite the market slowing down and have customers in every eCommerce vertical. We’re a passionate team of technologists who love solving problems and want to make our customers’ and coworkers’ lives better. We value empathy, openness, curiosity, continuous improvement, and are excited by metrics that matter. We believe that empowering everyone in a company to do what they think is best can lead to great things. Job Summary We’re looking for a systems-minded Staff Product Manager to take Constructor’s search and discovery to the state of the art — and keep it there — across every retail industry we serve. This is a highly technical, high-leverage role at the core of our Product Discovery Engine chapter: the teams responsible for how we retrieve, understand, and rank the right products for every shopper, on every query, for every customer. Your job is to raise discovery quality and, through it, our customers’ business metrics. You won’t be measured on features shipped — you’ll be measured on relevance and business outcomes, like conversion, revenue per visit, and search-attributed GMV, moving up. You’ll start embedded with our Search Quality team, owning its strategy and roadmap and getting deep into the systems that decide what shoppers actually see. Over time, your mandate grows to setting direction for the entire Product Discovery Engine chapter — deciding where we’re heading and why across recall, ranking, query understanding, personalization, and the ML infrastructure underneath. This is a Staff role today with a clear path to Director as you take on chapter-wide leadership. Search at Constructor is fundamentally a machine learning problem, so this role lives and breathes ML. You’ll partner closely with ML, data science, and engineering to turn the latest research into production wins, keep us ahead of both commercial competitors and academic state of the art, and be the connective tissue that aligns multiple teams — ML Recall, ML Infra, and, over time, teams like CORE, SABR, and Catalog Management — behind a single, coherent vision for great discovery. If you’re excited about driving innovation across dozens of the world’s largest retailers, being held to relevance and business outcomes, and pushing an already best-in-class product further ahead, this role is for you. 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. 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: • Driven measurable lift in search-attributed conversion and revenue per visit — moving business metrics, not just quality metrics. • Driven measurable business-metric lift (conversion, revenue per visit, GMV) and relevance lift across every major retail vertical we serve. • Shipped at least one state-of-the-art ML / LLM-assisted capability into production that beats our previous best on relevance. • Won head-to-head quality benchmarks against every major competitor in the verticals we compete in, with a repeatable, defensible methodology. • Taken core quality metrics to best-in-class levels and kept them improving quarter over quarter. • Stood up an evaluation framework and tooling that ties model and system investments directly to customer business outcomes. 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 interpret