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Descripción del puesto:
Our team's mission is to empower content understanding for TikTok Short Video business. We focus on cutting-edge research in content understanding and the development of advanced LLM/MLLM algorithms and applications, including generative recommendation, weakly-supervised learning, few-shot classification, video tagging, multi-task learning, multilingual learning, multimodal pretraining, and more. We aim to succeed both in driving measurable business impact (e.g., recommendation metrics) and delivering state-of-the-art research outputs. 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. Watch your ambitions become 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 * This opening is part of the general hiring process for the TikTok Data/Algorithm team. Applications will be evaluated by multiple teams within the TikTok Data/Algorithm team to ensure the best alignment based on skills and interests. Responsibilities 1. Lead multimodal algorithm development for TikTok's short-video business, explore applications of multimodal technologies in recommendation systems and other scenarios to improve key business metrics. 2. Conduct cutting-edge research in multimodal and MLLM technologies, design advanced algorithms to solve business requirements while achieving technical breakthroughs. 3. Drive engineering deployment and implementation, ensuring model stability, scalability, and efficiency in production environments. 4. Focus on key areas including (but not limited to): - General AI platform design and development, including few-shot/zero-shot on MLLM, AI-labeling, auto prompting, active-learning, continue pretraining and RL. - Integration of content understanding with recommendation systems (e.g., UGC ecosystems, cold start, interest exploration, comment understanding). - Leveraging multimodal techniques to develop next-generation recommendation systems, such as generative models and end-to-end approaches
Requerimientos del candidato/a:
Minimum Qualifications: 1. Proven experience in multimodal content understanding, with expertise in large language models (LLMs) and familiarity with cutting-edge progress in the field. 2. Strong technical foundation in at least one major deep learning framework (e.g., PyTorch, TensorFlow). 3. Proactive mindset, strong sense of ownership, excellent communication skills, and ability to collaborate across teams. 4. Currently pursuing a Master degree with a background in computer science, machine learning, or similar fields. 5. Able to commit to working for 12 weeks during Summer 2026. Preferred Qualifications: 1. Hands-on experience deploying content understanding solutions in search, advertising, recommendation, or related domains. For TikTok 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
| Origen: | Web de la compañía |
| Publicado: | 15 Ago 2025 (comprobado el 14 Dic 2025) |
| Tipo de oferta: | Prácticas |
| Sector: | Internet / Nuevos Medios |
| Idiomas: | Inglés |
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