Software Engineer - Recommendation Infrastructure, Performance Efficiency
Jun 9, 2026 San Jose
About us
TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.
- Size: 10,000+ employees
- Industry: Technology: Consumer Internet
Why you should apply for a job to TikTok:
4.5/5 in overall job satisfaction
4.5/5 in supportive management
100% say women are treated fairly and equally to men
100% would recommend this company to other women
100% say the CEO supports gender diversity
Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›
Responsibilities
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.
Qualifications
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.
Job Information
[For Pay Transparency] Compensation Description (annually)
The base salary range for this position in the selected city is $162000 - $387600 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.
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.
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.
Qualifications
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.
Job Information
[For Pay Transparency] Compensation Description (annually)
The base salary range for this position in the selected city is $162000 - $387600 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.
Responsibilities
ResponsibilitiesAbout 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.
Qualifications
QualificationsMinimum 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.
Additional Information
- Salary: $162,000 – $387,600
- Posted: Jun 9, 2026