Asks for

The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”

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 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.

What You’ll Do

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.

What Success Looks Like

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.

Benefits

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.