Research Scientist, Large-Scale ML & Recommender Systems

TikTok Inc.; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Benefits offered by this job

Medical, Dental, Vision

401(k) with company match

Parental leave

Disability coverage

Life insurance

Wellbeing benefits

Paid holidays

Paid sick days

Paid personal time

Job summary

TikTok Inc. is seeking graduates to join the Data-Arch team in 2027, working on end-to-end design, development and optimization of the recommendation infrastructure powering our global platforms.

You will tackle real-time performance, scalability, and model acceleration challenges across search, ads and recommendation domains. Successful candidates will contribute to building robust data pipelines, globalized recommendation systems, and high-performance computing frameworks, while enjoying

Qualifications

• Minimum qualification: PhD in Software Development, Computer Science, Computer Engineering or related field.
• Research background in ML/AI with focus on inference optimization, model acceleration, or efficient ML systems.
• Programming experience in C, C++, Java or Python.

Responsibilities

• Build and maintain high performance online services for TikTok recommendation and search system.
• Build efficient data pipelines for candidates generation, profile generation, training examples, realtime online training.
• Build globalized large-scale recommendation system.
• Design and develop high performance computing frameworks and storage systems.

Skills

C

C++

Java

Python

ML/AI

Education

PhD in Software Development, Computer Science, or Computer Engineering

Job description

TikTok Inc. is seeking graduates to join the Data-Arch team in 2027, working on end-to-end design, development and optimization of the recommendation infrastructure powering our global platforms.

You will tackle real-time performance, scalability, and model acceleration challenges across search, ads and recommendation domains. Successful candidates will contribute to building robust data pipelines, globalized recommendation systems, and high-performance computing frameworks, while enjoying