Backend Engineer

We are seeking a skilled Backend Engineer to contribute to the development of our search product. The ideal candidate will have experience in designing, building, and deploying large-scale distributed backend systems with a focus on scalability, availability, and fault tolerance.

As a key member of our team, you will work closely with applied ML engineers and catalog data teams to enable intelligent, contextual, and scalable search capabilities. You will be responsible for enhancing and evolving high-performance search backend systems, leading and participating in initiatives to modernize traditional keyword search by incorporating query intent understanding, semantic and vector-based retrieval and integrating LLM-driven components.

You will also build scalable batch and streaming data pipelines to enrich product catalog data and search index, optimize latency, reliability, and observability of search endpoints, evaluate search performance using offline metrics and live A/B experiments, driving continuous improvement.

Drive architectural decisions and mentor engineers through design reviews and code reviews, uphold engineering best practices, including performance monitoring, documentation, and operational support, stay current with the latest research and innovations in recommendation systems and search-related ML technologies, translating them into scalable production solutions.

Key Responsibilities:

• Enhance and evolve high-performance search backend systems, including query processing, document indexing, retrieval and ranking, ElasticSearch clusters, and scalable APIs.
• Lead and participate in initiatives to modernize traditional keyword search by incorporating query intent understanding, semantic and vector-based retrieval and integrating LLM-driven components.
• Build scalable batch and streaming data pipelines to enrich product catalog data and search index.
• Optimize latency, reliability, and observability of search endpoints.
• Evaluate search performance using offline metrics and live A/B experiments, driving continuous improvement.
• Drive architectural decisions and mentor engineers through design reviews and code reviews.
• Uphold engineering best practices, including performance monitoring, documentation, and operational support.
• Stay current with the latest research and innovations in recommendation systems and search-related ML technologies, translating them into scalable production solutions.

Requirements:

• Bachelor's or Master's in Computer Science, Software Engineering, Machine Learning, or related field.
• 5+ years of industry experience designing, building, and deploying large-scale distributed backend systems with a focus on scalability, availability, and fault tolerance.
• Hands-on experience with big data technologies such as Apache Beam, Pub/Sub, and Redis.
• Strong programming skills in JavaScript/Node.js, Python familiarity with Scala or Golang is a plus.
• Experience with search platforms (e.g., ElasticSearch), as well as SQL and NoSQL databases.
• Experience with API design and development using GraphQL and REST.
• Working knowledge of cloud infrastructure (e.g., GCP, AWS), Kubernetes, Docker, CI/CD, observability tooling.
• Strong communication and collaboration skills, with the ability to work effectively across teams and contribute to a high-performing engineering culture.

Preferred Qualifications:

• Experience in search or ML-related fields such as semantic or vector-based retrieval, NLP, or recommendation systems.
• Exposure to deep learning architectures, LLM integration, fine-tuning, or ML model deployment.
• Familiarity with ML system design, including model lifecycle management and experimentation pipelines.