Machine Learning Engineer, Content Understanding

TikTok s immersive experience, global presence, and high engagement makes it the ideal destination for brands, big and small, to showcase their unique identity, connect with their consumers, and build strong lasting relationships over time. Our Brand Ads team focuses on enabling brands to grow and succeed on TikTok by creating cutting-edge monetization products that empowers users, creators, and advertisers.

The Brand Ads Team builds technologies that unlock business growth potential. This team owns several ads products: reservation ads, auction ads, and innovative content ads that enables advertisers and users to foster more awareness of their brand to attain their business goals. We work on the end-to-end ads delivery tech stack, including ads bidding, ranking, and forecasting.

We are looking for seasoned engineers that have strong problem solving skills and algorithm understanding to build and manage systems with high performance, scalability, and availability. You will have the opportunity to partner closely with a globalized engineering and product teams in a high-impact and fast-paced environment.

What You ll Do:

• Participate in the development of a large-scale Ads system.
• Responsible for the development of state-of-the-art applied machine learning projects.
• Own key targeting components or strategies in the TikTok ads monetization ecosystem.
• Work with product and business teams on product vision. Minimum Qualifications:
• Experience in and good theoretical grounding in machine learning concepts and techniques.
• Proven experience in multimodal content understanding, with expertise in large language models (LLMs) and familiarity with cutting-edge progress in the field.
• Strong technical foundation in at least one major deep learning framework (e.g., PyTorch, TensorFlow).
• Proactive mindset, strong sense of ownership, excellent communication skills, and ability to collaborate across teams.

Preferred Qualifications:

• Hands-on experience deploying content understanding solutions in search, advertising, recommendation, or related domains.