AI Infra Engineer Graduate (Recommendation & LLM) - 2027 Start

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

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Paid parental leave

Short-term and long-term disability

Life insurance

Wellbeing benefits

10 paid holidays per year

10 paid sick days per year

17 days Paid Personal Time

Job summary

TikTok Inc. is seeking a Software Engineer for the Model Infrastructure team in California, focusing on next-generation infrastructure for For You recommendations and foundation models. You will work on distributed training, online inference, and GPU optimization to support billions of personalized videos daily.

The role emphasizes collaboration with researchers and engineers to deploy scalable LLM training and serving solutions across massive-scale systems.

Qualifications

• Bachelor's degree in Computer Science or related field.
• Strong programming skills in C++ or Python with solid CS fundamentals.
• Familiarity with PyTorch or TensorFlow.

Responsibilities

• Build and optimize infrastructure for large-scale model training and online inference.
• Develop distributed systems supporting large recommendation models and LLMs.
• Improve training and inference performance through GPU optimization and efficient communication.
• Collaborate with researchers to develop and deploy LLM training and serving solutions.
• Analyze system bottlenecks and implement performance optimizations.

Skills

C++

Python

Data structures

Algorithms

PyTorch

TensorFlow

LLMs

Transformer

Education

Bachelor's degree in Computer Science

Tools

PyTorch

TensorFlow

Job description

Responsibilities

About the Team We are looking for a passionate Software Engineer to join our Model Infrastructure team, building the next generation of infrastructure for TikTok's For You recommendation system and Large Language Models (LLMs). Our team develops the core training and serving infrastructure that powers one of the world's largest recommendation systems, enabling billions of personalized recommendations every day. We are also advancing the next generation of AI infrastructure for foundation models and LLMs, driving innovation in large-scale model training, online inference, and GPU optimization. As part of the team, you will work on distributed training and inference systems, high-performance GPU computing, and scalable LLM infrastructure. You'll collaborate closely with experienced engineers and researchers to transform cutting-edge AI technologies into production systems that directly impact the experience of hundreds of millions of TikTok users. This role is ideal for candidates who are passionate about LLM systems, distributed computing, GPU programming, and building AI systems at massive scale.

Responsibilities

• Build and optimize infrastructure for large-scale model training and online inference.
• Develop distributed systems supporting large recommendation models and LLMs.
• Improve training and inference performance through GPU optimization and efficient communication.
• Collaborate with researchers to develop and deploy LLM training and serving solutions.
• Analyze system bottlenecks and implement performance optimizations.

Qualifications

• Individuals who are completing or have recently completed a Bachelor's degree in Computer Science or a related discipline.
• Strong programming skills in C++ or Python.
• Good understanding of data structures, algorithms, and computer systems.
• Familiarity with PyTorch or TensorFlow.
• Knowledge of Transformer architectures and Large Language Models (LLMs).
• Strong problem-solving skills and a passion for building large-scale AI systems.

Preferred Qualifications

• Hands-on experience with LLM training or inference through research, internships, or open-source projects.
• Familiarity with distributed training concepts (e.g., DP, TP, PP, FSDP, ZeRO).
• Experience with GPU programming using CUDA, Triton, or similar technologies.
• Understanding of LLM serving techniques such as KV Cache, Continuous Batching, or FlashAttention.
• Contributions to open-source projects or research in machine learning systems, distributed systems, or LLM infrastructure.

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.