Responsibilities

At TikTok, we treat privacy as our top priority in our product design and implementation. Privacy is not just about regulation compliance, but also about a more trusted way to enable technology innovation by respecting users' privacy choices! About the Team Privacy Innovation (PI) Lab is established to explore the next frontier of privacy technology and theory in the digitalized world. We provide key insights and technical solutions on privacy-related innovation for all TikTok's products. Furthermore, we also collaborate with worldwide technical and academic communities to build an open ecosystem to promote a privacy-friendly digital experience. We are looking for talented individuals to join us for an internship in 2026. PhD Internships at our company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. PhD internships at our company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community - building and development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis. We encourage you to apply early. Please state your availability clearly in your resume (Start date, End date). About the Role We are building next-generation generative foundation models, with a strong focus on diffusion-based and unified generation-understanding architectures, deployed in privacy-sensitive, production environments. This role sits at the intersection of - Large-scale model training systems - GPU-first architecture and kernel-level optimization - Diffusion / DiT / unified multimodal foundation models - Privacy-preserving and compliant training pipelines You will work on end-to-end training architecture design, from model-parallel execution and GPU efficiency to robust, fault-tolerant, privacy-aware training infrastructure. Responsibilities: You will work directly on core operators and system-level performance optimization for large-scale models, including but not limited to: 1. Design and implement high-performance GPU kernels for core components such as: Transformer / Attention / MoE / Diffusion 2. Perform end-to-end optimization for large model training workloads 3. Conduct in-depth analysis of GPU execution bottlenecks, including compute, memory, and scheduling 4. Use and extend Triton / CUDA / CUTLASS, and integrate optimized kernels with PyTorch / XLA / custom runtimes 5. Collaborate closely with model research teams to: Translate new model architectures into efficient, production-ready implementations 6. Reproduce, benchmark, and improve state-of-the-art system optimization techniques, validating gains in real training and inference settings

Qualifications

Minimum Qualifications 1. Currently pursuing PhD in Computer science, computer engineering, or a related technical discipline. 2. Solid understanding of GPU architecture and execution models 3. Proficiency in CUDA C++ or Triton, with the ability to independently write and optimize kernels 4. Strong familiarity with Transformer / Attention computation patterns and performance bottlenecks 5. Ability to read, reproduce, and reason about systems papers or open-source implementations Preferred Qualifications 1. Hands-on experience with large-scale model training 2. Familiarity with PyTorch internals (e.g., Autograd, dispatcher, ATen) 3. Experience with kernel profiling and performance tuning (e.g., Nsight, nvprof, nsys) 4. Publications, open-source contributions, or performance benchmark results By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy

Job Information