Company Overview
TikTok is the leading global platform for short-form video, designed to ignite creativity and inspire imagination.
At TikTok, our teams thrive on curiosity and operate at the pace of culture. With a flat organizational structure, employees are empowered with dynamic opportunities to make a real impact in a rapidly growing company while advancing their careers.
With offices across Asia Pacific, the Middle East, Europe, and the Americas, we are just getting started—join us on this exciting journey.
About the Team
The Responsible Recommendation System Team is dedicated to advancing content understanding and recommendation techniques to enhance TikTok’s content safety and ecosystem. Our focus is on developing scalable, personalized solutions that protect user experiences while driving meaningful product impact.
Responsibilities – What You’ll Do
- Develop and refine personalization models to enhance user recommendations, addressing challenges such as long-tail distributions, sparse data, and biased objectives.
- Innovate on machine learning algorithms that drive one of the world’s most advanced recommendation systems, ensuring continuous optimization of TikTok’s content ecosystem.
- Design and implement end-to-end machine learning solutions, covering the entire model lifecycle—including data collection, feature engineering, model deployment, and performance evaluation.
- Collaborate with cross-functional teams, working alongside engineers from diverse technical backgrounds to transform technical advancements into impactful product improvements.
- Utilize data-driven methodologies and conduct rigorous experimentation to analyze, measure, and optimize model performance.
Qualifications
Minimum Requirements
- Bachelor’s degree in Computer Science or a related field (advanced degrees preferred).
- Strong expertise in machine learning, with hands-on experience developing and deploying personalization models in production environments.
- Proficiency in programming languages such as Python or C++, with experience in leading machine learning frameworks.
- Solid understanding of information retrieval techniques and their application in large-scale recommendation systems.
- Proven ability to solve complex machine learning challenges involving long-tail, sparse, or biased data distributions.
- Excellent communication and collaboration skills, with a track record of effectively working within cross-functional teams.
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