Machine Learning Engineer Graduate (TikTok Vertical Recommendation) - 2027 Start

About the Team The Recommendation Architecture team powers personalized recommendations for TikTok's vertical businesses. Our team consists of machine learning engineers and system engineers who support and innovate on production recommendation models serving hundreds of millions of users. We work at the intersection of machine learning and large-scale distributed systems, in a fast-paced, collaborative and impact-driven culture where your work directly shapes what users discover on TikTok every day.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. 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 our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities - Implement, train, and iterate on retrieval and ranking models (candidate generation, coarse/fine ranking, re-ranking) for TikTok vertical-business recommendation scenarios, in close partnership with algorithm teams. - Deliver end-to-end machine learning engineering solutions: from model implementation and feature engineering on large-scale user behavior data to online deployment. - Run and analyze A/B experiments; drive launches that improve core business metrics. - Productionize state-of-the-art techniques such as sequence modeling, multi-task/multi-objective learning, and LLM-enhanced recommendation in large-scale systems. - Collaborate closely with algorithm teams, backend engineers, data scientists, and product teams.

Minimum Qualifications - Individuals who are completing or have recently completed a Bachelor's degree in Computer Science or a related discipline. - Solid foundation in data structures, algorithms, and machine learning fundamentals. - Proficiency in at least one general-purpose programming language such as Python, C++, Go, or Java. - Strong communication and collaboration skills; curiosity about technology and problem-solving.

Preferred Qualifications - Internship or research experience in recommendation, search, or ads; publications in relevant venues are a plus. - Hands-on experience with a deep learning framework such as PyTorch or TensorFlow. - Experience with large-scale data processing (Spark/Flink) or distributed model training. - Agile and quick learner with self-motivation, a sense of ownership, and creative problem-solving abilities.