Software Engineer Intern (Trust and Safety – Algorithm Engineering) – 2026 Summer (BS/MS)

Responsibilities

We are the Algorithm Engineering Team from the Data-TnS department. As our name suggests, we are a diverse group committed to using our engineering skills to accelerate model iteration and production. On the other hand, we utilize various tools to enhance the efficiency of integrating our models into our business scenarios. Our team’s goal is ‘Let TnS’s algorithmic capabilities cover wherever TikTok needs them. We are looking for talented individuals to join us for an internship in 2026. Internships at TikTok aim to offer students industry exposure and hands-on experience. Turn your ambitions into reality as your inspiration brings infinite opportunities at TikTok. Internships at TikTok aim to provide students with hands-on experience in developing fundamental skills and exploring potential career paths. A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth. It runs for 12 weeks. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The Application limit is applicable to TikTok and its affiliates’ jobs globally. Applications will be reviewed on a rolling basis. We encourage you to apply as early as possible. Please state your availability clearly in your resume (Start date, End date). Summer Start Dates: – May 11th, 2026 – May 18th, 2026 – May 26th, 2026 – June 8th, 2026 – June 22nd, 2026 Online Assessment Candidates who pass resume screening will be invited to participate in TikTok’s technical online assessment. Responsibilities: 1. Work closely with business teams to optimize the integration plan for algorithm applications, improve efficiency in evaluating and using algorithm applications across various business scenarios, and reduce the cost of managing and optimizing algorithm applications in different business scenarios. 2. Be responsible for the architectural design, development, and performance tuning of algorithm applications, solving technical challenges such as high concurrency, high reliability, and high scalability. Work includes multiple sub-areas: ML model training and evaluation, model optimization, model inference, model management, dataset management, workflow orchestration, etc. 3. Responsible for the design and development of Machine Learning infrastructure for LLM/AIGC, etc 4. Be responsible for researching and implementing cutting-edge engineering technologies related to LLM, NLP, CV.

Qualifications

Minimum Qualifications: 1. Currently pursuing a Undergraduate Degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline. 2. Familiar with one or two programming languages, such as C++, Go, or Python, and knowledgeable about CUDA or deep learning frameworks (such as PyTorch, Deepspeed, Megatron, vllm, etc.). 3. Understanding of the principles of distributed systems, large-scale data processing, and parallel computing 4. Interested and experienced in one or more of the following areas: machine learning, deep learning, computational acceleration, and performance optimization. 5. Familiar with the ML Infrastructure of Large Model training and inference Preferred Qualifications: 1. Excellent programming skills, data structure and algorithm skills, proficient in C/C++ or Python programming language, candidates with awards in ACM/ICPC, NOI/IOI, Top Coder, Kaggle and other competitions are preferred. 2. Research or industry experience in the field of machine learning, especially in large language models (LLMs) and generative artificial intelligence. 3. Distributed training framework optimizations such as DeepSpeed, FSDP, Megatron, GSPMD 4. Experiences in in-depth CUDA programming and performance tuning (cutlass, triton) 5. Experience with evaluation of ML models, LLM application & agent development is desirable. 6. Understanding cutting-edge LLM research and engineering (e.g., long context, multi modality, active learning, alignment research, agent ecosystem, etc.) and possess practical expertise in effectively implementing these advanced systems.

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