Job description

About The Team: The Recommendation System Infrastructure team is responsible for building and evolving the large-scale online serving and data infrastructure that powers TikTok’s recommendation products globally. Our mission is to deliver highly efficient, reliable, observable, and scalable infrastructure for recommendation systems. The team works closely with recommendation algorithm teams to accelerate strategy iteration, improve compute efficiency, optimize serving cost, and enable the next generation of AI-native and agentic engineering workflows. We focus on core infrastructure challenges across online/nearline/offline modules on GPU/CPU, high-performance computing, data pipelines, observability, automation, system reliability, and cost optimization. Our systems are primarily built in C++, while broader infrastructure and automation work may also involve offline data processing frameworks such as Flink, Spark, or other large-scale data systems. A key direction of the team is to build 24/7 closed-loop agentic systems that can observe, diagnose, plan, execute, verify, and continuously improve recommendation infrastructure and iteration workflows. Responsibilities: - Design, build, and optimize high-performance online serving systems for large-scale global recommendation systems, improving business ROI, system efficiency, and serving quality. - Improve the efficiency, reliability, scalability, and cross-regional consistency of recommendation system infrastructure. - Identify and resolve system performance bottlenecks across CPU, memory, bandwidth, GPU compute efficiency, serving latency, throughput, and resource allocation efficiency. - Drive cost optimization for large-scale recommendation serving, including business-impact-based cost efficiency, compute resource utilization, and infrastructure-level or strategy-level performance improvements. - Build reliable and efficient workflows and pipelines for automation on candidate generation, profile generation, feature processing, training data generation, and online development. Minimum Qualifications: - Bachelor’s degree or above in Computer Science, Software Engineering, or a related technical field. - Experience in building scalable backend systems, distributed systems, infrastructure systems, or high-performance online services. - Strong programming skills in at least one systems programming language, such as C++, C, Go, or Java. - Solid understanding of data structures, algorithms, operating systems, networking, and distributed system fundamentals. - Experience with performance analysis, system debugging, reliability improvement, or large-scale service optimization. - Strong ownership, problem-solving ability, and communication skills. - Ability to work effectively with cross-functional teams, including infrastructure teams, recommendation algorithm teams, and product/business-facing engineering teams. Preferred Qualifications: - Experience with infrastructure for recommendation systems, search engines, advertising systems, machine learning systems, or large-scale online serving systems. - Experience optimizing high-throughput, low-latency C++ services in production environments. - Familiarity with profiling, benchmarking, performance tuning, capacity planning, resource efficiency improvement, and cost optimization. - Experience with large-scale data processing systems such as Flink, Spark, Kafka, or similar frameworks. - Experience building end-to-end automation systems based on AI agents, LLMs, workflow orchestration, or closed-loop engineering automation. - Experience designing agentic workflows for system diagnosis, performance optimization, reliability improvement, change validation, or automatic execution. - Experience with real-time data pipelines, online training, feature engineering, candidate generation, or recommendation system iteration workflows.

Requirements

Must-haves

The Recommendation System Infrastructure team is responsible for building and evolving the large-scale online serving and data infrastructure that powers TikTok’s recommendation products globally.
Design, build, and optimize high-performance online serving systems for large-scale global recommendation systems, improving business ROI, system efficiency, and serving quality.
Improve the efficiency, reliability, scalability, and cross-regional consistency of recommendation system infrastructure.
Identify and resolve system performance bottlenecks across CPU, memory, bandwidth, GPU compute efficiency, serving latency, throughput, and resource allocation efficiency.
Drive cost optimization for large-scale recommendation serving, including business-impact-based cost efficiency, compute resource utilization, and infrastructure-level or strategy-level performance improvements.
Build reliable and efficient workflows and pipelines for automation on candidate generation, profile generation, feature processing, training data generation, and online development.
Bachelor’s degree or above in Computer Science, Software Engineering, or a related technical field.
Experience in building scalable backend systems, distributed systems, infrastructure systems, or high-performance online services.
Strong programming skills in at least one systems programming language, such as C++, C, Go, or Java.
Solid understanding of data structures, algorithms, operating systems, networking, and distributed system fundamentals.
Experience with performance analysis, system debugging, reliability improvement, or large-scale service optimization.
Strong ownership, problem-solving ability, and communication skills.
Ability to work effectively with cross-functional teams, including infrastructure teams, recommendation algorithm teams, and product/business-facing engineering teams.

Nice-to-haves

Experience with infrastructure for recommendation systems, search engines, advertising systems, machine learning systems, or large-scale online serving systems.
Experience optimizing high-throughput, low-latency C++ services in production environments.
Familiarity with profiling, benchmarking, performance tuning, capacity planning, resource efficiency improvement, and cost optimization.
Experience with large-scale data processing systems such as Flink, Spark, Kafka, or similar frameworks.
Experience building end-to-end automation systems based on AI agents, LLMs, workflow orchestration, or closed-loop engineering automation.
Experience designing agentic workflows for system diagnosis, performance optimization, reliability improvement, change validation, or automatic execution.
Experience with real-time data pipelines, online training, feature engineering, candidate generation, or recommendation system iteration workflows.

Application prep

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Keywords to mirror

aigojavamachine learning

Platform: tiktok
Effort: medium