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Senior Backend Engineer: Recommendations

💼 Full-time🗓 2026-06-25

Core

Design, deliver, and maintain highload real-time web services for search and discovery in ecommerce, powering recommendations and shopping agents.

Role type

Senior Backend Engineer (Recommendations)

Builds

Highload real-time web services, io-bound and cpu-bound services, data services, and cloud-deployed solutions.

Domain

Ecommerce, Search & Discovery, AI/LLMs

Deliverable

production ML models | product features | infrastructure

Required skills

Highload real-time web services, AWS CloudFormation, Jenkins, GitHub Actions, Service observability, Monitoring metrics, Alerting tools, CI/CD pipelines, Stability testing

Preferred skills

NoSQL databases, Python, Internal and external service integration

Technologies

AWS CloudFormation, Jenkins, GitHub Actions, Prometheus, Grafana, PagerDuty, AWS CloudWatch, Python, NoSQL

Responsibilities

Build and deploy robust recommendations services; Write infrastructure automation scripts; Set up service observability and alerting; Implement CI/CD pipelines and stability testing.

Seniority

Senior, hands-on IC

Rewrite
## About the role A primary focus of this job is to design, deliver & maintain highload real-time web services in close collaboration with other great engineers both from recommendations & other teams. ## Responsibilities - Build / deploy / support robust recommendations services including io-bound web services, cpu-bound services and data services - Write AWS CloudFormation scripts, Jenkins jobs, Github actions following best industry standards - Set up service observability, monitoring metrics, and alerting (Prometheus, Grafana, PagerDuty, AWS CloudWatch) - Implement CI/CD pipelines and separate stability testing for recommendations needs ## Requirements - Experience with highload real-time web services - Proficiency in writing AWS CloudFormation scripts, Jenkins jobs, Github actions - Familiarity with service observability, monitoring metrics, and alerting tools (Prometheus, Grafana, PagerDuty, AWS CloudWatch) - Ability to implement CI/CD pipelines and stability testing for recommendations needs ## About the company Constructor is the next-generation platform for search and discovery in ecommerce, 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 ecommerce companies in the world like Sephora, Under Armour, and Petco. We're a passionate team 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 do best can lead to great things. 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. ## About the Team The Recommendations team plays an important role in improving the experience of our customers & end-users. We're a passionate team of cross-functional engineers who love challenges and want to make people's lives better. We value openness, curiosity, continuous improvement, and great code. We believe that empowering everyone in a company to do what they think is best can lead to great things. Our team is committed to perfecting traditional methods of product discovery while spearheading new avenues with the assistance of large language models, ushering in a new era of innovation and user engagement. The Recommendation team manages multiple endpoints within shared services, alongside its own cloud-deployed solutions developed in Python, which require leveraging NoSQL databases and interfacing with both internal and external services. As part of our roadmap, we are planning to separate all main developments into distinct high-load distributed services.
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