Senior Machine Learning Engineer: Ranking (remote) - Remote Eligible

Constructor Ranking Team Machine Learning Engineer

Constructor is the next-generation platform for search and discovery in e-commerce, built to explicitly optimize for metrics like revenue, conversion rate, and profit. Our search engine is entirely invented in-house utilizing transformers and generative LLMs, and we use its core and personalization capabilities to power everything from search itself to recommendations to shopping agents. Engineering is by far our largest department, and we've built our proprietary engine to be the best on the market, having never lost an A/B test to a competitive technology. We're passionate about maintaining this and work on the bleeding edge of AI to do so.

Out of necessity, our engine is built for extreme scale and powers over 1 billion queries every day across 150 languages and roughly 100 countries. It is used by some of the biggest e-commerce companies in the world like Sephora, Under Armour, and Petco.

Constructor is a U.S. based company that has been in the market since 2019. It was founded by Eli Finkelshteyn and Dan McCormick who still lead the company today.

The Ranking team, within the Machine Learning chapter, plays a central role in implementing algorithms that optimize our customers' business KPIs like revenue and conversion rates. We focus on metrics over features, supplying our ranking algorithms with powerful capabilities that bring value to our customers.

As a member of the Ranking team, you will be encouraged to use world-class analytical, engineering, and machine learning techniques on big data to scale our ranking algorithms. The Ranking team owns all stages of product ranking for Constructor's Search, Browse, and Autocomplete experiences, including base ranking, ML ranking, personalization, and ranking explanation.

A primary focus of the Ranking team is to develop a high-quality ranking system that satisfies business needs and accounts for behavioral user patterns. Related to that focus, the Ranking team owns:

• An online high load distributed REST based ranking service deployed in the cloud and developed in the Python programming language, receiving around 55 million requests a day.
• Offline Data Pipelines that are used for data processing (Python, Spark/ Databricks), ML model training and model signals delivery (e.g. Feature Store), Ranking configuration for any given customer.
• Ranking Quality monitoring tools to measure relevance, personalization, attractiveness, diversification, and other quality signals.

As a Machine Learning Engineer on the Ranking team, your primary focus will be to enhance the quality of our ranking systems, ensuring that search, browse, and autocomplete experiences are highly relevant, personalized, and diverse. You will work on building state-of-the-art ranking algorithms that improve user experience and drive critical business metrics such as conversion, user engagement, and revenue growth.

In addition to improving ranking quality, you will ensure that our solutions can be deployed in real-time environments, handling high-throughput requests efficiently while maintaining low-latency performance. Our ranking system processes thousands of requests per second, and maintaining both quality and speed is essential for our global customers, who rely on fast, accurate results.

The job can consist of, but is not limited to:

• Design and Develop ML-Based Ranking Solutions: build, deploy, and optimize machine learning models to enhance search engine ranking systems, driving improvements in key business metrics such as conversion, engagement, and user satisfaction.
• Improve Ranking Quality: analyze ranking performance and identify gaps in search, browse, and autocomplete experiences, focusing on relevance, personalization, attractiveness, diversification, and other quality signals.
• Innovate and Optimize Ranking Algorithms: proactively propose new machine learning models, algorithms, and features to advance the ranking pipeline, improve ranking quality, and meet evolving business needs.
• Collaboration with Cross-Functional Teams: collaborate with technical and non-technical business partners to develop / update ranking functionalities (both within and outside the team)