Machine Learning Engineer, E-commerce Feed Recommendation

$137,750 - $237,500/Yr

Tiktok - Seattle, WA

Full-time - Entry Level

Seattle, WA

Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services

About the position

As a Machine Learning Engineer focused on E-commerce Feed Recommendation at TikTok, you will be part of a dynamic team dedicated to enhancing the shopping experience for users through innovative machine learning algorithms. TikTok is not just a platform for short-form mobile videos; it is a rapidly growing e-commerce destination that connects customers with high-quality products and sellers through personalized recommendations. Your role will involve developing and optimizing recommendation systems that cater to billions of users daily, ensuring that they receive tailored content that aligns with their interests. In this position, you will participate in building large-scale recommendation algorithms for live-streaming and short videos, which are crucial for TikTok's unique shopping experience. You will design, develop, evaluate, and iterate on predictive models that enhance candidate generation and ranking, focusing on metrics such as Click Through Rate (CTR) and Conversion Rate (CVR). This will include constructing real-time data pipelines, performing feature engineering, and innovating model optimization techniques. Additionally, you will be responsible for creating long and short-term user interest models, analyzing vast amounts of data to extract relevant insights, and designing algorithms that efficiently explore users' latent interests. Your work will directly impact user engagement and satisfaction, addressing e-commerce challenges like the cold start problem and optimizing traffic allocation. You will also have the opportunity to design and build supporting tools to facilitate debugging and enhance system performance.

Responsibilities

• Participate in building large-scale live-streaming and short video e-commerce recommendation algorithms and systems on TikTok.
• Design, develop, evaluate and iterate on predictive models for candidate generation and ranking, including building real-time data pipelines, feature engineering, and model optimization.
• Build long and short term user interest models, analyze and extract relevant information from large amounts of data, and design algorithms to explore users' latent interests efficiently.
• Design and develop various strategies using ML technology to improve user shopping experience and resolve e-commerce business challenges, such as the cold start problem and traffic allocation.
• Design and build supporting/debugging tools as needed.

Requirements

• Bachelor's degree or higher in Computer Science or related fields.
• Strong programming and problem-solving ability.
• 1 year of experience in applied machine learning, familiar with algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, Wide and Deep.
• Experience in Deep Learning Tools such as TensorFlow/PyTorch.
• Experience with at least one programming language like C++/Python or equivalent.

Nice-to-haves

• 2 years of experience in recommendation systems, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.
• Publications at KDD, NeurIPS, WWW, SIGIR, WSDM, ICML, IJCAI, AAAI, RECSYS and related conferences/journals, or experience in data mining/machine learning competitions such as Kaggle/KDD-cup.

Benefits

• 100% premium coverage for employee medical insurance, approximately 75% premium coverage for dependents.
• Health Savings Account (HSA) with a company match.
• Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life and AD&D insurance plans.
• Flexible Spending Account (FSA) Options like Health Care, Limited Purpose and Dependent Care.
• 10 paid holidays per year plus 17 days of Paid Personal Time Off (PPTO) and 10 paid sick days per year.
• 12 weeks of paid Parental leave and 8 weeks of paid Supplemental Disability.
• Mental and emotional health benefits through EAP and Lyra.
• 401K company match, gym and cellphone service reimbursements.