Senior Backend Engineer - Marketplace Intelligence & Data (Shared Components)
Summary
Build high-performance ML infrastructure and distributed systems for vector search, indexing, and agent workflows in a large-scale ecommerce marketplace.
Job Description
Collaborate with algorithm and backend engineers to design and build high-performance machine learning infrastructure, tooling, and frameworks. Develop scalable solutions to drive one of the following key initiatives: Vector Search & Indexing Agent Workflows Orchestration
Requirements
Bachelor's degree in Computer Science or a related technical discipline. In-depth understanding of computer science fundamentals (data structures and algorithms, operating systems, networks, databases, etc.). Strong, hands-on experience with at least one of the following programming languages: Go, C++, or Java.
Preferred Qualifications
Experience in designing and deploying machine learning-related backend services. Experience in the architecture and development of large-scale distributed systems. Experience with big data pipelines and compute frameworks (e.g., Spark, Flink, Ray). Familiarity with LLM and agentic ecosystems (e.g., vLLM, RAG, MCP).
Collaborate with algorithm and backend engineers to design and build high-performance machine learning infrastructure, tooling, and frameworks. Develop scalable solutions to drive one of the following key initiatives: Vector Search & Indexing Agent Workflows Orchestration
Requirements
Bachelor's degree in Computer Science or a related technical discipline. In-depth understanding of computer science fundamentals (data structures and algorithms, operating systems, networks, databases, etc.). Strong, hands-on experience with at least one of the following programming languages: Go, C++, or Java.
Preferred Qualifications
Experience in designing and deploying machine learning-related backend services. Experience in the architecture and development of large-scale distributed systems. Experience with big data pipelines and compute frameworks (e.g., Spark, Flink, Ray). Familiarity with LLM and agentic ecosystems (e.g., vLLM, RAG, MCP).