Machine Learning Engineer Graduate (eCommerce Recommendation) - 2025 Start (BS/MA)

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us
Creation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible.
Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day.
To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
At TikTok, we create together and grow together. That's how we drive impact - for ourselves, our company, and the communities we serve.
Join us.

Our E-commerce Recommendation Team is responsible for building up and scaling our recommendation system to provide the best shopping experience for our TikTok users.

We are looking for talented individuals to join our team in 2025. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.

Successful candidates must be able to commit to an onboarding date by end of year 2025.

We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume.

Applications will be reviewed on a rolling basis. We encourage you to apply early.

Candidates can apply for a maximum of TWO positions and will be considered for jobs in the order you applied for. The application limit is applicable to TikTok and its affiliates' jobs globally.

Responsibilities
• Work in a team to conduct cutting-edge research in machine learning algorithms, such as retrieval and recommendation algorithms
• Participate in building large-scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok
• Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently
• Apply machine learning algorithms to improve the different business scenarios, such as search ranking, natural language and video understanding, and trust and safetyMinimum Qualifications:
• Current or recent graduates with a background in Computer Science, related technical field or equivalent industry experience
• Experience in applied machine learning, familiar with one or more of the algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, Wide and Deep etc.
• Experience in Deep Learning Tools such as tensorflow/pytorch
• Experience with at least one programming language like C++/Python or equivalent
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://shorturl.at/cdpT2.