AI Infrastructure Engineer - Recommendation & LLM
Aug 25, 2026 San Jose
About us
TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.
- Size: 10,000+ employees
- Industry: Technology: Consumer Internet
Why you should apply for a job to TikTok:
4.5/5 in overall job satisfaction
4.5/5 in supportive management
100% say women are treated fairly and equally to men
100% would recommend this company to other women
100% say the CEO supports gender diversity
Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›
Responsibilities
About the Team
We are looking for experienced Software Engineers / ML Systems Engineers to join our Model Infrastructure team and build the next generation of AI infrastructure powering TikTok's For You recommendation system and Large Language Models (LLMs).
Our team develops the core training and serving infrastructure behind one of the world's largest recommendation systems, enabling billions of personalized recommendations every day. We are also building next-generation infrastructure for foundation models and LLMs, covering large-scale model training, online inference, GPU optimization, distributed systems, and AI serving.
As a member of the team, you will take ownership of challenging infrastructure problems at massive scale, working across model, framework, runtime, GPU, distributed systems, and production serving. You will collaborate closely with researchers, algorithm engineers, and infrastructure teams to turn state-of-the-art AI technologies into highly scalable, reliable, and efficient production systems.
This role is ideal for engineers who are passionate about AI systems, distributed computing, GPU optimization, Recommendation, LLMs, and building high-performance infrastructure at massive scale.
Responsibilities
- Design, develop, and optimize large-scale AI training and online inference infrastructure for recommendation models and LLMs.
- Drive the architecture and implementation of distributed training and serving systems with high scalability, reliability, and efficiency.
- Optimize end-to-end model performance across GPU computation, communication, memory, networking, and runtime systems.
- Develop and optimize LLM training and serving infrastructure, including model parallelism, KV Cache, Continuous Batching, and efficient inference.
- Work closely with researchers and algorithm engineers to productionize new model architectures and algorithms.
- Identify and resolve performance bottlenecks across the full AI stack, from model and framework to GPU kernels and distributed runtime.
- Improve system latency, throughput, GPU utilization, scalability, and infrastructure cost efficiency.
- Drive technical design, implementation, performance benchmarking, and production rollout of critical infrastructure components.
- Mentor junior engineers and contribute to the team's technical direction and engineering standards.
Qualifications
Minimum Qualifications
- Bachelor's, Master's, or Ph.D. degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
- 3+ years of software engineering, ML systems, distributed systems, or related industry experience.
- Strong programming skills in C++ or Python and experience building production-quality software.
- Strong understanding of data structures, algorithms, operating systems, distributed systems, or computer architecture.
- Hands-on experience with PyTorch, TensorFlow, or other machine learning frameworks.
- Experience designing, developing, or optimizing large-scale production systems.
- Strong problem-solving skills and the ability to independently drive complex technical projects.
Preferred Qualifications
- Experience building infrastructure for large-scale recommendation systems, LLMs, or foundation models.
- Strong understanding of distributed training and model parallelism, including DP, TP, PP, FSDP, ZeRO, or related technologies.
- Hands-on experience with GPU programming and optimization, including CUDA, Triton, GPU kernels, or similar technologies.
- Experience with LLM inference and serving, including KV Cache, Continuous Batching, FlashAttention, CUDA Graph, speculative decoding, or related techniques.
- Experience optimizing GPU utilization, memory efficiency, communication performance, latency, and throughput at scale.
- Experience with distributed systems, high-performance computing, networking, or ML infrastructure.
- Experience designing and owning large-scale production systems from architecture through deployment and operation.
- Contributions to open-source projects, research publications, or technical projects in machine learning systems, distributed systems, GPU computing, or LLM infrastructure.
- Experience mentoring engineers or leading technical projects is a plus.
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.
About the Team
We are looking for experienced Software Engineers / ML Systems Engineers to join our Model Infrastructure team and build the next generation of AI infrastructure powering TikTok's For You recommendation system and Large Language Models (LLMs).
Our team develops the core training and serving infrastructure behind one of the world's largest recommendation systems, enabling billions of personalized recommendations every day. We are also building next-generation infrastructure for foundation models and LLMs, covering large-scale model training, online inference, GPU optimization, distributed systems, and AI serving.
As a member of the team, you will take ownership of challenging infrastructure problems at massive scale, working across model, framework, runtime, GPU, distributed systems, and production serving. You will collaborate closely with researchers, algorithm engineers, and infrastructure teams to turn state-of-the-art AI technologies into highly scalable, reliable, and efficient production systems.
This role is ideal for engineers who are passionate about AI systems, distributed computing, GPU optimization, Recommendation, LLMs, and building high-performance infrastructure at massive scale.
Responsibilities
- Design, develop, and optimize large-scale AI training and online inference infrastructure for recommendation models and LLMs.
- Drive the architecture and implementation of distributed training and serving systems with high scalability, reliability, and efficiency.
- Optimize end-to-end model performance across GPU computation, communication, memory, networking, and runtime systems.
- Develop and optimize LLM training and serving infrastructure, including model parallelism, KV Cache, Continuous Batching, and efficient inference.
- Work closely with researchers and algorithm engineers to productionize new model architectures and algorithms.
- Identify and resolve performance bottlenecks across the full AI stack, from model and framework to GPU kernels and distributed runtime.
- Improve system latency, throughput, GPU utilization, scalability, and infrastructure cost efficiency.
- Drive technical design, implementation, performance benchmarking, and production rollout of critical infrastructure components.
- Mentor junior engineers and contribute to the team's technical direction and engineering standards.
Minimum Qualifications
- Bachelor's, Master's, or Ph.D. degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
- 3+ years of software engineering, ML systems, distributed systems, or related industry experience.
- Strong programming skills in C++ or Python and experience building production-quality software.
- Strong understanding of data structures, algorithms, operating systems, distributed systems, or computer architecture.
- Hands-on experience with PyTorch, TensorFlow, or other machine learning frameworks.
- Experience designing, developing, or optimizing large-scale production systems.
- Strong problem-solving skills and the ability to independently drive complex technical projects.
Preferred Qualifications
- Experience building infrastructure for large-scale recommendation systems, LLMs, or foundation models.
- Strong understanding of distributed training and model parallelism, including DP, TP, PP, FSDP, ZeRO, or related technologies.
- Hands-on experience with GPU programming and optimization, including CUDA, Triton, GPU kernels, or similar technologies.
- Experience with LLM inference and serving, including KV Cache, Continuous Batching, FlashAttention, CUDA Graph, speculative decoding, or related techniques.
- Experience optimizing GPU utilization, memory efficiency, communication performance, latency, and throughput at scale.
- Experience with distributed systems, high-performance computing, networking, or ML infrastructure.
- Experience designing and owning large-scale production systems from architecture through deployment and operation.
- Contributions to open-source projects, research publications, or technical projects in machine learning systems, distributed systems, GPU computing, or LLM infrastructure.
- Experience mentoring engineers or leading technical projects is a plus.
About the Team
We are looking for experienced Software Engineers / ML Systems Engineers to join our Model Infrastructure team and build the next generation of AI infrastructure powering TikTok's For You recommendation system and Large Language Models (LLMs).
Our team develops the core training and serving infrastructure behind one of the world's largest recommendation systems, enabling billions of personalized recommendations every day. We are also building next-generation infrastructure for foundation models and LLMs, covering large-scale model training, online inference, GPU optimization, distributed systems, and AI serving.
As a member of the team, you will take ownership of challenging infrastructure problems at massive scale, working across model, framework, runtime, GPU, distributed systems, and production serving. You will collaborate closely with researchers, algorithm engineers, and infrastructure teams to turn state-of-the-art AI technologies into highly scalable, reliable, and efficient production systems.
This role is ideal for engineers who are passionate about AI systems, distributed computing, GPU optimization, Recommendation, LLMs, and building high-performance infrastructure at massive scale.
Responsibilities
- Design, develop, and optimize large-scale AI training and online inference infrastructure for recommendation models and LLMs.
- Drive the architecture and implementation of distributed training and serving systems with high scalability, reliability, and efficiency.
- Optimize end-to-end model performance across GPU computation, communication, memory, networking, and runtime systems.
- Develop and optimize LLM training and serving infrastructure, including model parallelism, KV Cache, Continuous Batching, and efficient inference.
- Work closely with researchers and algorithm engineers to productionize new model architectures and algorithms.
- Identify and resolve performance bottlenecks across the full AI stack, from model and framework to GPU kernels and distributed runtime.
- Improve system latency, throughput, GPU utilization, scalability, and infrastructure cost efficiency.
- Drive technical design, implementation, performance benchmarking, and production rollout of critical infrastructure components.
- Mentor junior engineers and contribute to the team's technical direction and engineering standards.
Qualifications
Minimum Qualifications
- Bachelor's, Master's, or Ph.D. degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
- 3+ years of software engineering, ML systems, distributed systems, or related industry experience.
- Strong programming skills in C++ or Python and experience building production-quality software.
- Strong understanding of data structures, algorithms, operating systems, distributed systems, or computer architecture.
- Hands-on experience with PyTorch, TensorFlow, or other machine learning frameworks.
- Experience designing, developing, or optimizing large-scale production systems.
- Strong problem-solving skills and the ability to independently drive complex technical projects.
Preferred Qualifications
- Experience building infrastructure for large-scale recommendation systems, LLMs, or foundation models.
- Strong understanding of distributed training and model parallelism, including DP, TP, PP, FSDP, ZeRO, or related technologies.
- Hands-on experience with GPU programming and optimization, including CUDA, Triton, GPU kernels, or similar technologies.
- Experience with LLM inference and serving, including KV Cache, Continuous Batching, FlashAttention, CUDA Graph, speculative decoding, or related techniques.
- Experience optimizing GPU utilization, memory efficiency, communication performance, latency, and throughput at scale.
- Experience with distributed systems, high-performance computing, networking, or ML infrastructure.
- Experience designing and owning large-scale production systems from architecture through deployment and operation.
- Contributions to open-source projects, research publications, or technical projects in machine learning systems, distributed systems, GPU computing, or LLM infrastructure.
- Experience mentoring engineers or leading technical projects is a plus.
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.
Responsibilities
ResponsibilitiesAbout the Team
We are looking for experienced Software Engineers / ML Systems Engineers to join our Model Infrastructure team and build the next generation of AI infrastructure powering TikTok's For You recommendation system and Large Language Models (LLMs).
Our team develops the core training and serving infrastructure behind one of the world's largest recommendation systems, enabling billions of personalized recommendations every day. We are also building next-generation infrastructure for foundation models and LLMs, covering large-scale model training, online inference, GPU optimization, distributed systems, and AI serving.
As a member of the team, you will take ownership of challenging infrastructure problems at massive scale, working across model, framework, runtime, GPU, distributed systems, and production serving. You will collaborate closely with researchers, algorithm engineers, and infrastructure teams to turn state-of-the-art AI technologies into highly scalable, reliable, and efficient production systems.
This role is ideal for engineers who are passionate about AI systems, distributed computing, GPU optimization, Recommendation, LLMs, and building high-performance infrastructure at massive scale.
Responsibilities
- Design, develop, and optimize large-scale AI training and online inference infrastructure for recommendation models and LLMs.
- Drive the architecture and implementation of distributed training and serving systems with high scalability, reliability, and efficiency.
- Optimize end-to-end model performance across GPU computation, communication, memory, networking, and runtime systems.
- Develop and optimize LLM training and serving infrastructure, including model parallelism, KV Cache, Continuous Batching, and efficient inference.
- Work closely with researchers and algorithm engineers to productionize new model architectures and algorithms.
- Identify and resolve performance bottlenecks across the full AI stack, from model and framework to GPU kernels and distributed runtime.
- Improve system latency, throughput, GPU utilization, scalability, and infrastructure cost efficiency.
- Drive technical design, implementation, performance benchmarking, and production rollout of critical infrastructure components.
- Mentor junior engineers and contribute to the team's technical direction and engineering standards.
Qualifications
QualificationsMinimum Qualifications
- Bachelor's, Master's, or Ph.D. degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
- 3+ years of software engineering, ML systems, distributed systems, or related industry experience.
- Strong programming skills in C++ or Python and experience building production-quality software.
- Strong understanding of data structures, algorithms, operating systems, distributed systems, or computer architecture.
- Hands-on experience with PyTorch, TensorFlow, or other machine learning frameworks.
- Experience designing, developing, or optimizing large-scale production systems.
- Strong problem-solving skills and the ability to independently drive complex technical projects.
Preferred Qualifications
- Experience building infrastructure for large-scale recommendation systems, LLMs, or foundation models.
- Strong understanding of distributed training and model parallelism, including DP, TP, PP, FSDP, ZeRO, or related technologies.
- Hands-on experience with GPU programming and optimization, including CUDA, Triton, GPU kernels, or similar technologies.
- Experience with LLM inference and serving, including KV Cache, Continuous Batching, FlashAttention, CUDA Graph, speculative decoding, or related techniques.
- Experience optimizing GPU utilization, memory efficiency, communication performance, latency, and throughput at scale.
- Experience with distributed systems, high-performance computing, networking, or ML infrastructure.
- Experience designing and owning large-scale production systems from architecture through deployment and operation.
- Contributions to open-source projects, research publications, or technical projects in machine learning systems, distributed systems, GPU computing, or LLM infrastructure.
- Experience mentoring engineers or leading technical projects is a plus.
Additional Information
- Salary: $128,000 – $316,800
- Posted: Aug 25, 2026