Constructor is the only search and product discovery platform tailor-made for enterprise ecommerce where conversions matter. Constructors AI-first solutions make it easier for shoppers to discover products they want to buy and for ecommerce teams to deliver highly personalized experiences that drive impressive results. Optimizing specifically for ecommerce metrics like revenue, conversion rate and profit, Constructor generates consistent $10M+ lifts for some of the biggest brands in ecommerce, such as Sephora, Petco, home24, Maxeda Brands, Birkenstock and The Very Group. Constructor is a U.S. based company that was founded in 2015 by Eli Finkelshteyn and Dan McCormick. For more, visit: constructor.io.

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.

The team is cross-functional, consisting of ML, BE, and FE engineers as well as designers. 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.

Challenges you will tackle

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)

Requirements

Hard skills

At least 4 years of experience with Python for machine learning and backend development.
At least 4 years of experience developing, deploying, and maintaining machine learning models with a strong focus on ranking systems for search, recommendations, or similar applications.
Experience in large-scale ML model training, evaluation, and optimization, with a focus on real-time inference and serving.
Experience with big data frameworks such as Spark for processing large datasets and integrating them into ML pipelines.
Proficiency in using tools like SQL, PySpark, Pandas, and other frameworks to extract, manipulate, and analyze data
Experience with data pipeline orchestration tools like Airflow or Luigi to manage and automate workflows for ML training and signal delivery.
Experience working on ranking algorithms that optimize metrics such as relevance, conversion rates, personalization, user engagement, RPV is a plus.

Soft skills

Experience collaborating in cross-functional teams.
Experience leading projects to success
Excellent English communication skills.
Enjoy helping others around you grow as developers and be successful
Pick up new ideas and technologies quickly, love learning and talking to others about them
Love to experiment and use data and customer feedback to drive decision making