Senior Search Backend Engineer

Join our Discovery Team as a Senior Search Backend Engineer, where you will build the retrieval layer behind product discovery for leading commerce brands. You will work on cutting-edge search technologies, including semantic and hybrid retrieval, and contribute to search relevance and quality. This is a remote-first position with flexible working hours and a comprehensive benefits package.

Run and maintain a scalable search infrastructure on top of Kubernetes using terraform and multi-tenant architecture.

Hands-on search experience, preferably ElasticSearch or OpenSearch, including a working understanding of how an inverted index scores and ranks documents

About commercetools

You're a creative problem-solver who is wired to find solutions. You confidently dive into complex challenges and have a talent for making them simple for others. Your curiosity drives you to constantly grow and contribute to an environment of trust and teamwork

Responsibilities

• Design and implement search functionalities in a PaaS ecommerce Scala backend, focusing on lexical, semantic, and hybrid retrieval.
• Develop search-related libraries based on ElasticSearch and MongoDB, contributing to search relevance and quality.

Requirements

• Experience operating multi-tenant SaaS where one tenant’s load must never affect another’s
• Interest in working with Scala using pragmatic functional programming patterns. If you come from another JVM language, we will support you in picking it up
• Solid understanding of parallel and asynchronous programming, as well as non-blocking I/O
• At least 5 years of experience as a Software Engineer, with production experience on the JVM

Fluent English for work in international teams

• A genuine curiosity for using AI tools to work smarter and more effectively, paired with a drive to learn and put them into practice in your role
• Knowledge of scalable event-driven architectures, including queues and notification topics
• Experience developing REST APIs and knowledge of scalable architectures, including sharding, replication, load balancing, and failover
• Great ideas come from many paths, and your unique perspective matters more than checking every box. What matters most is the mindset you bring to the work
• You do not need to be an ML researcher for this role. You need to be a strong backend engineer who is genuinely interested in retrieval quality
• Practical exposure to vector retrieval, such as embeddings, approximate nearest neighbor indexes, and hybrid ranking, and a sense of where each approach breaks down. A deep lexical search background plus a clear appetite to go into this territory also works
• Experience with infrastructure work and other technologies like Helm, Kubernetes, and Rust
• Comfort reasoning about relevance with data, including precision and recall tradeoffs, ranking metrics, and behavioral signals