Senior Platform R&D Engineer - Recommendation System

Senior Platform R&D Engineer - Recommendation System at TikTok in Singapore.

Responsibilities

1. Design intelligent development toolkits for large-scale recommendation systems, providing tooling and productized solutions to enhance R&D efficiency.
2. Develop business metrics-driven gray release systems to ensure safe, stable, and efficient release strategies and workflows.
3. Enhance observability of recommendation systems in complex global environments (multi-region, multi-data center, multi-language), establish end-to-end tracing systems, and optimize issue attribution mechanisms.
4. Build algorithm engineering toolchains to accelerate the end-to-end process from experimental algorithm/model development to deployment, improving iteration efficiency.
5. Overhaul platform ecosystem architectures and develop data intelligence assistants.

Requirements

1. Bachelor's degree or above, majoring in Computer Science, or related fields, with 3+ years of experience in designing and developing large-scale systems.
2. Proficiency in at least one programming language (Go/Python/C++) and solid understanding of data structures and algorithms.
3. Ability to independently lead the technical design and implementation of complex systems and components.
4. Strong product sense, with the ability to balance priorities across requirements, technical solutions, timelines, and delivery quality.

Preferred Qualifications

1. Deep understanding of large language models (LLMs), familiarity with LLM principles and applications, and hands-on experience in Agent system design.
2. Experience in recommendation/advertising/search systems, or privacy compliance frameworks (GDPR, CCPA).

About the Company

The Recommendation Architecture Team is responsible for designing and developing recommendation system architectures for various products under the company. We ensure system stability and high availability, optimize performance for online services and offline data pipelines, address system bottlenecks, and reduce operational costs. We also abstract reusable system components and services to build recommendation and data middleware platforms, supporting rapid incubation of new products and empowering enterprise clients (ToB).