About the Team
At Trendyol Tech, our mission is to create a positive impact in our ecosystem by enabling commerce through technology.
We solve complex problems with data, creativity, and agility — always driven by real outcomes. With a culture built on learning, collaboration, and ownership, we grow together while building what’s next.
About the Role
As a Backend Engineer in the Machine Learning Group, you will build the systems that turn raw data into smart, reliable, and production-ready features used across our AI models and applications. You’ll design and develop scalable data and backend services that support model training, feature storage, and real-time inference. Working closely with Data Scientists and other engineers, you’ll ensure our models get high-quality, consistent inputs that improve performance and enable data-driven business decisions.
Responsibilities
Design and implement robust and scalable backend services using modern software stack.Collaborate closely with product managers to ensure seamless integration and meet business requirements.Write clean, maintainable, and efficient code.Develop well-designed APIs to interface with frontend services, databases, and other third-party services.Monitor system performance and identify bottlenecks, bugs, and ways to improve backend efficiency and speed.Implement security and data protection best practices.Conduct code reviews, ensuring high-quality code.Stay updated with current best practices in software development and introduce them to our processes.
Expected Qualifications
3+ years of experience in backend development, with a focus on Go, Java, kotlin, .Net etc.Strong knowledge of modern programming languages, paradigms, constructs, and idioms.Strong understanding of system architecture, object-oriented design, and design patterns.Knowledge of common concurrency and async communication patterns.Experience with databases (SQL or NoSQL)Experience with working on high volume of data.Proficient understanding of code versioning tools, such as Git.Knowledge and practical usage of CI/CD pipelines
Nice-to-havesKnowledge of Docker and KubernetesKnowledge of Feature StoresExperience on Flink DataStream frameworkML engineering practices including but not limited to model training, ML pipelines and inference services
