AI Infra Optimization Engineer Graduate (TikTok Global E-Commerce Recommendation & Search Architecture) - 2027 Start

TIKTOK PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Stock options

Yoga & fitness

Flexible hours

Paid leave

Flat organization

Career growth opportunities

Competitive compensation

Job summary

TikTok PTE. LTD. in Singapore is seeking talents to advance live and recommendation technologies. You will deeply integrate LLMs with real-time recommendation systems, build scalable model infrastructures, and optimize training and inference across multi-GPU clusters.

Ideal candidates have strong C++/CUDA/Python skills, experience with distributed training, and a proven ability to turn research into production. Join a fast-growing team driving cutting-edge AI experiences for a global audience.

Qualifications

• Completed or recent graduate in AI/CS/CE or related field.
• Strong communication and ownership traits.
• Experience in collaborating across cross-functional teams.

Responsibilities

• Contribute to building and optimizing LLM-enabled recommendation systems.
• Develop and optimize model training and inference pipelines.
• Collaborate with algorithm teams on research and productionization.

Skills

Effective communication

Ownership and drive

Education

Bachelor's or Master's degree in AI/CS/CE or related

Tools

C++

CUDA

Triton

Python

Java

Golang

Job description

About TikTok

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us

Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.

We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

Job highlights

Yoga and fitness, Stock options, Positive team atmosphere, Career growth opportunity, Paid leave, Flat organization, 100+ mil users, Industry experts, Competitive compensation, Flexible hours

Responsibilities

Team Introduction:

E-commerce is a new and fast growing business that aims at connecting all customers to excellent sellers and quality products, through E-commerce live-streaming, E-commerce short videos, and commodity recommendation. Our E-commerce Recommendation Infra team is responsible for building up and optimizing the infrastructure for such recommendation systems, so as to provide the 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.

Project introduction

With the rapid development of LLM technologies, traditional deep learning algorithms and system architectures for recommendation are facing both transformational challenges and opportunities:

• (1) Lifelong Behavior Modeling and Model Scale-up
• (2) End-to-End Generative Recommendation

We are looking for talented individuals to join our team.

Responsibilities

1. 1. Deeply integrate LLM and recommendation technologies; develop, deploy, and optimize LLM training and inference techniques for recommendation foundation models; and solve large-model engineering challenges in recommendation scenarios.
2. 2. Provide highly automated, agentic, and performance-oriented model optimization solutions for frameworks such as PyTorch.
3. 3. Build, design, and implement next-generation model infra architectures based on open-source components like Vllm/SGlang/Megatron/VeRL, enabling recommendation foundation models to be deployed in production.
4. 4. Co-design with algorithm teams to research, innovate, and productionize end-to-end generative recommendation technologies.
5. 5. Research Areas:
   • (1) LLM-native recommendation models and training/inference architectures
     • Re-architect recommendation models and the complete training/inference technology stack through secondary development based on PyTorch and LLM components.
     • Ultra-long-context modeling of users' lifelong behavioral histories.
     • KV cache sharing across multiple user browsing sessions.
     • Multimodal recommendation foundation models and downstream fine-tuning tasks.
   • (2) Automated, agentic, and extreme-performance model optimization
     • Throughput optimization for high-performance GPU kernels and multi-GPU parallelism.
     • Extreme performance optimization tailored to the specialized structures of recommendation models.
     • Agentic paradigms for performance optimization.
     • Exploration of new AI infrastructure paradigms enabled by AI-powered productivity.
   • (3) Next-generation recommendation system architectures built on foundation models
     • Shared modeling and shared computation across retrieval, pre-ranking, ranking, and re-ranking stages.
     • Prefill/decode disaggregation and computation graph partitioning tailored to recommendation workloads.
     • LLM architectures that support the sparse characteristics of recommendation scenarios.
   • (4) Innovation in end-to-end generative recommendation
     • Alignment between multimodal LLM foundations and recommendation tasks.
     • Implementation, deployment, and optimization of reinforcement learning techniques in recommendation scenarios.
     • Data and sample construction for generative recommendation.
     • Exploration of new recommendation paradigms and technical approaches.

Qualifications

Minimum Qualifications

• Individuals who are completing or have recently completed a Bachelor's or Master's degree in Artificial Intelligence, Software Development, Computer Science, Computer Engineering or a related discipline.
• Solid programming skills in C++/CUDA/Triton/Python.
• Familiarity with GPU architecture and distributed training is highly desirable.
• Experience programming in at least one of the following programming languages: C, C++, Java or Golang.
• Effective communication skills and a sense of ownership and drive.

Preferred Qualifications

• Strong programming and algorithmic foundations, with proficiency in languages such as C/C++, Python. Experience with CUDA development and familiarity with TensorRT, Triton, or CUTLASS is preferred.
• Familiarity with research and technical developments in LLM inference acceleration, including but not limited to model quantization, graph compilation, operator optimization, KV cache optimization, and prefill/decode disaggregation.
• Hands-on knowledge of LLM training and inference technologies, with practical development and production experience using systems such as Megatron-LM, DeepSpeed, vLLM, SGLang, or TensorRT-LLM.
• Extensive experience and broad technical perspective in foundation model engineering, close awareness of open-source developments, and strong independent troubleshooting capabilities.
• Experience in recommendation, advertising, or search model development and optimization, or contributions to open-source foundation model communities, is a plus.