Senior Backend Engineer, Location-Based Services

re-zoo-me; Singapore; Hybrid; SGD 120,000 - 180,000; Full time

Job summary

TikTok is seeking an engineer to advance its LBS platform by building and optimizing geolocation microservices, data pipelines, and online/offline processing. The role focuses on high-concurrency services and scalable data workflows within a collaborative team.

You will work on geolocation inference, location fusion, and RAG-based workflows, contributing to a robust, scalable system used by billions of users. The team emphasizes learning and innovation.

Qualifications

• Bachelor’s degree in Computer Science or a related field.
• Familiarity with at least one programming language such as Java, Golang, PHP, Python, or C++, with a preference for Golang or Java.
• Experience with building high‑concurrency services.

Responsibilities

• Build fused geolocation capabilities by integrating multiple signal sources (IP, Wi‑Fi, GPS) to support TikTok Local Services and other products.
• Build Nearby and Location Profile Services to support TikTok Local Services and other location-based experiences.
• Develop and optimize core geolocation microservices, including online storage, geolocation inference, and location fusion services, to support high-QPS requests from multiple upstream services.
• Participate in building an online streaming data scheduling system for high performance, scalability, and reliability of data scheduling.
• Design, build, and evaluate LLM-powered workflows (prompt engineering, RAG, tool use, agent orchestration) to improve feature engineering efficiency.

Skills

Java

Golang

Python

C++

Education

Bachelor’s degree in Computer Science or related field

Tools

Linux

MySQL

NoSQL

RPC

MQ

Caching

Microservices

Job description

TikTok is seeking an engineer to advance its LBS platform by building and optimizing geolocation microservices, data pipelines, and online/offline processing. The role focuses on high-concurrency services and scalable data workflows within a collaborative team.

You will work on geolocation inference, location fusion, and RAG-based workflows, contributing to a robust, scalable system used by billions of users. The team emphasizes learning and innovation.