Backend Engineer Graduate (TikTok Vertical Recommendation Architecture) - 2027 Start

TikTok Inc.; San Jose (CA); On-site; USD 128,000 - 317,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with company match

Paid parental leave

Disability insurance (short-term &

Job summary

TikTok Inc. is seeking graduates to join the Recommendation Architecture Team in San Jose, CA, to design and optimize the recommendation system architecture for TikTok’s vertical businesses.

You will focus on stability, scalability, and performance across online services and offline data flows. The role offers growth through bold ideas and challenging projects, with opportunities to work closely with algorithm teams.

Qualifications

• Bachelor's degree in Computer Science or related discipline.
• Strong programming skills with good code design and style; familiarity with Go, C++, Java, or Python.
• Solid understanding of data structures, algorithms, OS, networking, and distributed/scalable systems design.

Responsibilities

• Participate in the design of the recommendation system to enhance development efficiency, performance, scalability, and recommendation effectiveness.
• Build and maintain high-performance, high-availability online services for the TikTok recommendation system.
• Build efficient and reliable data pipelines for candidate generation, profile generation, and training-example generation.
• Pursue extreme performance optimization in storage, computing, or application layers; optimize latency, throughput, and costs at scale.
• Troubleshoot production issues and build mechanisms for operational stability, including capacity planning, disaster recovery, and monitoring.
• Collaborate with ML engineers to productionize models efficiently and safely.

Skills

Go

C++

Java

Python

Data structures

Algorithms

Operating systems

Networking

Distributed systems

Redis

Kafka

Education

Bachelor's degree in Computer Science or related discipline

Tools

RPC frameworks

Job description

Responsibilities

About the Team

Our Recommendation Architecture Team is responsible for building up and optimizing the architecture of the recommendation system for TikTok's vertical businesses, providing the most stable and best experience for TikTok users. We focus on optimizing the recommendation system architecture, ensuring stability and high availability, and improving the performance of both online services and offline data flows. We work closely with algorithm teams to improve recommendation effectiveness while boosting performance and reducing costs.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.

Responsibilities

• Participate in the design of the recommendation system to enhance development efficiency, performance, scalability, and recommendation effectiveness.
• Build and maintain high-performance, high-availability online services (retrieval, ranking orchestration, feature and index services) for the TikTok recommendation system.
• Build efficient and reliable data pipelines for candidate generation, profile generation, and training-example generation.
• Pursue extreme performance optimization in the storage layer, computing layer, or application layer; optimize latency (P99), throughput, and resource cost at scale.
• Troubleshoot production issues and build mechanisms that ensure operational stability, including capacity planning, disaster recovery, and monitoring.
• Collaborate with machine learning engineers to productionize models efficiently and safely.

Qualifications

Minimum Qualifications

• Individuals who are completing or have recently completed a Bachelor's degree in Computer Science or a related discipline.
• Strong programming skills with good code design and style; familiarity with at least one of Go, C++, Java, or Python.
• Solid understanding of data structures, algorithms, operating systems, computer networking, and distributed/scalable systems design.

Preferred Qualifications

• Interest in recommendation systems, with an understanding of their principles or execution processes.
• Internship experience in performance optimization or architecture work in high-traffic scenarios.
• Familiarity with RPC frameworks, caching, or large-scale storage/messaging systems (Redis, Kafka, etc.).
• Agile and quick learner with self-motivation, a sense of ownership, and creative problem-solving abilities.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $128000 - $316800 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.