Machine Learning Engineer Graduate (Global E-Commerce Recommendation) - 2027 Start (PhD)

About the Team

Our team operates at the forefront of the booming E-commerce sector, striving to connect consumers with top-tier sellers and quality products via innovative methods such as live-streaming, short videos, and targeted commodity recommendations. We are a diverse group of applied machine learning professionals and data scientists focused on pushing the boundaries in E-commerce recommendations, applying large-scale machine learning technologies to solve practical challenges and translating creative concepts into impactful business results.

Role Overview

We invite recent or soon-to-be PhD graduates to join us in tackling complex problems and nurturing growth opportunities within this vibrant space. Successful candidates should be prepared to begin by the end of the calendar year, with their availability and graduation date clearly noted in their application materials.

Key Responsibilities

• Develop and enhance extensive recommendation algorithms and systems that operate on billions of data points, including those for commodities, live streams, and short videos.
• Model user interests, both immediate and long-term, to extract meaningful insights from vast, heterogeneous data sets and identify latent user preferences efficiently.
• Create, refine, and evaluate predictive models for candidate generation and ranking tasks, such as predicting click-through rates and conversion rates, incorporating real-time data pipelines, feature engineering, and model innovations.
• Design and maintain support and debugging utilities as required to ensure smooth operation.
• Deliver scalable and optimized AI and machine learning models for production environments.
• Focus on ETL (extract, transform, load) processes for large-scale, real-time, and unstructured data streams to deploy AI/ML solutions derived from theoretical research.
• Conduct experiments to evaluate model effectiveness, diagnose bugs, and implement necessary fixes in deployed models.
• Collaborate closely within a team, applying statistical, scripting, and programming expertise.
• Engage with the software platforms hosting these models to ensure proper deployment and functionality.

Qualifications

• PhD graduates or candidates completing doctoral studies in Software Development, Computer Science, Computer Engineering, or related fields.
• Strong capabilities in programming and complex problem-solving.
• Experience with applied machine learning and familiarity with algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, and Wide & Deep models.
• Proficiency with deep learning frameworks like TensorFlow or PyTorch.
• Competence in at least one programming language such as C++ or Python.

Preferred Qualifications

• Background in recommendation systems, online advertising, information retrieval, natural language processing, machine learning, or large-scale data mining.
• Publications in notable conferences or journals including KDD, NeurIPS, WWW, SIGIR, WSDM, ICML, IJCAI, AAAI, RecSys or participation in prominent data mining or machine learning competitions like Kaggle or KDD-cup.

About the Company

TikTok stands as a premier short-form mobile video platform. Headquartered in Los Angeles and Singapore, with global offices worldwide, its mission is to inspire creativity and spread joy through authentic expression and connection.

Why Join Us

At TikTok, creativity fuels our mission and diversity powers our teams. We foster a culture of curiosity, humility, resilience, and innovation where challenges are embraced and turned into opportunities for growth as one team. Adopting an “Always Day 1” mindset, we aim to achieve significant breakthroughs for our users, ourselves, and the company.

Diversity & Inclusion

We are deeply committed to cultivating an inclusive workplace where diverse experiences and perspectives are cherished. Our platform and culture are designed to connect global communities by celebrating varied voices and fostering an environment reflecting the users we serve.