Backend Software Engineer, TikTok Live Recommendation Infrastructure

TikTok Inc.; San Jose (CA); On-site; USD 254,000 - 588,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) savings plan with company match

Paid parental leave

Short-term disability coverage

Long-term disability coverage

Life insurance

Wellbeing benefits

10 paid holidays per year

10 paid sick days per year

17 days of Paid Personal Time (prorate

Job summary

TikTok Inc. is hiring for a role in the Live-stream Recommendation Infra team. You will design and operate backend systems that scale for training, inference, and data pipelines to support next‑gen recommendation workloads.

Collaborate with ML engineers to productionize models and optimize performance across training, inference, and data workflows in a high-traffic live streaming environment. Strong distributed systems experience is essential.

Qualifications

• Bachelor's degree or above in Computer Science, Engineering, or related technical field.
• At least 3 years of experience in strong programming skills in C++, Go, or Java, and scripting experience in Python.
• Solid experience in distributed systems and backend service development.
• Hands-on experience with ML infrastructure, including model serving, inference optimization, or large-scale training systems.
• Proficiency in building and maintaining data pipelines such as Spark, Flink, Kafka, Hadoop, or similar.
• Strong problem-solving skills, with the ability to work in fast-paced, collaborative environments.

Responsibilities

• Design and build backend systems that support large-scale recommendation workloads, including training, inference, and data pipelines.
• Develop robust and efficient model infrastructure, including distributed training pipelines and low-latency inference serving.
• Architect and improve data pipelines to enable efficient collection, preprocessing, and offline feature engineering for recommendation and ranking models.
• Collaborate closely with ML engineers and researchers to productionize models and integrate them into the TikTok Live recommendation stack.
• Drive performance optimization and cost-efficiency across training, inference, and data workflows.
• Ensure system robustness, scalability, and maintainability in high-traffic live streaming scenarios.

Skills

C++/Go/Java

Python scripting

Distributed systems

ML infrastructure

Data pipelines

Problem solving

Education

Bachelor's degree or above in Computer Science

Tools

Spark

Flink

Kafka

Hadoop

Kubernetes

Job description

Responsibilities

About the Team

Live-stream is a new and rapidly growing business that aims to bring joy to end users and allow more influencers to make an impact among their followers. And it's essential to pick the \"right\" live-stream for the \"right\" audiences. Our Live-stream Recommendation Infra team is responsible for building up and optimizing the infrastructure for such recommendation systems, so as to provide the most stable and best experience for our users. We work closely with applied machine learning engineers and build scalable systems to support all kinds of innovative algorithms and techniques.

• Design and build backend systems that support large-scale recommendation workloads, including training, inference, and data pipelines.


• Develop robust and efficient model infrastructure, including distributed training pipelines and low-latency inference serving.


• Architect and improve data pipelines to enable efficient collection, preprocessing, and offline feature engineering for recommendation and ranking models.


• Collaborate closely with ML engineers and researchers to productionize models and integrate them into the TikTok Live recommendation stack.


• Drive performance optimization and cost-efficiency across training, inference, and data workflows.


• Ensure system robustness, scalability, and maintainability in high-traffic live streaming scenarios.

Qualifications

Minimum Qualifications:

• Bachelor's degree or above in Computer Science, Engineering, or related technical field.


• At least 3 years of experience in strong programming skills in C++, Go, or Java, and scripting experience in Python.


• Solid experience in distributed systems and backend service development.


• Hands-on experience with ML infrastructure, including model serving, inference optimization, or large-scale training systems.


• Proficiency in building and maintaining data pipelines such as Spark, Flink, Kafka, Hadoop, or similar.


• Strong problem-solving skills, with the ability to work in fast-paced, collaborative environments.

Preferred Qualifications:

• Experience working with recommendation systems, ranking, or personalization platforms.


• Familiarity with deep learning frameworks such as TensorFlow, PyTorch.


• Knowledge of cloud-native environments (Kubernetes, container orchestration).


• Experience in performance optimization for large-scale, low-latency systems.


• Prior experience in live streaming, content delivery, or real-time systems is a plus.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $254400 - $588000 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.

• medical insurance


• dental insurance


• vision insurance


• 401(k) savings plan with company match


• paid parental leave


• short-term disability coverage


• long-term disability coverage


• life insurance


• wellbeing benefits


• 10 paid holidays per year


• 10 paid sick days per year


• 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:

• Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;


• Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems;


• Exercising sound judgment.

Responsibilities

About the Team

Live-stream is a new and rapidly growing business that aims to bring joy to end users and allow more influencers to make an impact among their followers. And it's essential to pick the \"right\" live-stream for the \"right\" audiences. Our Live-stream Recommendation Infra team is responsible for building up and optimizing the infrastructure for such recommendation systems, so as to provide the most stable and best experience for our users. We work closely with applied machine learning engineers and build scalable systems to support all kinds of innovative algorithms and techniques.

• Design and build backend systems that support large-scale recommendation workloads, including training, inference, and data pipelines.


• Develop robust and efficient model infrastructure, including distributed training pipelines and low-latency inference serving.


• Architect and improve data pipelines to enable efficient collection, preprocessing, and offline feature engineering for recommendation and ranking models.


• Collaborate closely with ML engineers and researchers to productionize models and integrate them into the TikTok Live recommendation stack.


• Drive performance optimization and cost-efficiency across training, inference, and data workflows.


• Ensure system robustness, scalability, and maintainability in high-traffic live streaming scenarios.

Qualifications

Minimum Qualifications:

• Bachelor's degree or above in Computer Science, Engineering, or related technical field.


• At least 3 years of experience in strong programming skills in C++, Go, or Java, and scripting experience in Python.


• Solid experience in distributed systems and backend service development.


• Hands-on experience with ML infrastructure, including model serving, inference optimization, or large-scale training systems.


• Proficiency in building and maintaining data pipelines such as Spark, Flink, Kafka, Hadoop, or similar.


• Strong problem-solving skills, with the ability to work in fast-paced, collaborative environments.

Preferred Qualifications:

• Experience working with recommendation systems, ranking, or personalization platforms.


• Familiarity with deep learning frameworks such as TensorFlow, PyTorch.


• Knowledge of cloud-native environments (Kubernetes, container orchestration).


• Experience in performance optimization for large-scale, low-latency systems.


• Prior experience in live streaming, content delivery, or real-time systems is a plus.