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. 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.

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 $244800 - $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. 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.