机器学习软件工程师

The Content Ecology Algorithm Team drives TikTok's AI innovations in LLMs/MLLMs, NLP, Computer Vision, Multimodal learning, Agentic AI, and recommendation algorithms.

We develop cutting-edge AI capabilities that power multiple business lines.

You will tackle unique algorithm challenges in building products for a global audience.

Our platform's diverse content and worldwide user base create a wealth of opportunities for AI applications.

With rapidly growing businesses such as TikTok Local Services, you'll have the chance to bring cutting-edge AI innovations into real-world production.

- Drive the research and development of core algorithms for TikTok Local Services, leveraging LLMs/MLLMs, Agentic AI, NLP, Computer Vision, conversion modeling, global optimization, and causal uplift modeling.

- Develop deep semantic representations and structured models of content, products, and user intent, while building foundational capabilities across product knowledge graphs, intent understanding, and end-to-end intelligent transaction optimization.

- Continuously enhance both supply- and demand-side experiences to drive sustainable growth in transaction conversion.

- Tackle frontier technical challenges unique to local services, including multilingual and multimodal understanding, highly dynamic environments, and long-horizon sequential decision-making.

- Stay at the forefront of research, translate state-of-the-art advances into production-grade solutions, scale proven innovations, and establish reusable algorithmic foundations with broad applicability.

- Bachelor's degree or above in Computer Science, Artificial Intelligence, Software Engineering or a related technical field.

- Strong foundations in machine learning and deep learning, with a solid understanding of Transformer architectures, LLMs, multimodal large language models, and modern representation learning techniques.

- Experience in one or more relevant areas, such as NLP, computer vision, multimodal understanding, AI agents, knowledge graphs, retrieval and recommendation, conversion modeling, causal uplift modeling, or decision optimization.

- Hands-on experience with Python, PyTorch, distributed model training, and large-scale data processing, with strong algorithmic, software engineering, and production deployment capabilities.

- Proven ability to formulate and solve complex, ambiguous problems, translate research advances into scalable production solutions, and deliver measurable business impact.

- Strong cross-functional collaboration and communication skills, with a track record of driving technically complex projects to high-quality execution.

- Experience building large-scale machine learning systems for e-commerce, recommendation, search, advertising, marketplaces or other transaction-oriented products.

- Experience working with multilingual, multimodal, or highly dynamic data and deploying models in large-scale production environments.

- Publications at leading conferences, such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, NAACL, KDD, WWW, SIGIR, RecSys, AAAI, Interspeech, or ICASSP.

- Demonstrated technical leadership through driving cross-functional initiatives, establishing reusable algorithmic capabilities, or mentoring engineers and researchers.