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.

Design and implement search functionalities in a PaaS ecommerce Scala backend, focusing on lexical, semantic, and hybrid retrieval.

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

About commercetools

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

Responsibilities

• Develop search-related libraries based on ElasticSearch and MongoDB, and contribute to search relevance and quality.

Requirements

• 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
• At least 5 years of experience as a Software Engineer, with production experience on the JVM
• 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

Fluent English for work in international teams

• Experience developing REST APIs and knowledge of scalable architectures, including sharding, replication, load balancing, and failover
• Experience operating multi-tenant SaaS where one tenant’s load must never affect another’s
• Knowledge of scalable event-driven architectures, including queues and notification topics
• 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
• 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
• Hands-on search experience, preferably ElasticSearch or OpenSearch, including a working understanding of how an inverted index scores and ranks documents
• Comfort reasoning about relevance with data, including precision and recall tradeoffs, ranking metrics, and behavioral signals
• 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

Preferred

• Experience with infrastructure work and other technologies like Helm, Kubernetes, and Rust