Machine Learning Engineer, E-commerce Feed Recommendation

$145,000 - $250,000/Yr

Tiktok - San Jose, CA

Full-time - Entry Level

San Jose, CA

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

About the position

As a Machine Learning Engineer for E-commerce Feed Recommendation at TikTok, you will be part of a dynamic team focused on enhancing the shopping experience through innovative recommendation systems. TikTok is at the forefront of connecting customers with high-quality products through personalized recommendations, leveraging the power of live-streaming and short videos. Your role will involve developing large-scale recommendation algorithms that cater to millions of users, ensuring that our platform remains engaging and user-friendly. You will participate in building and optimizing algorithms that predict user behavior, such as Click Through Rate (CTR) and Conversion Rate. This will require you to design and implement predictive models, develop real-time data pipelines, and engage in feature engineering to enhance the performance of our recommendation systems. Your work will directly impact how users interact with e-commerce content on TikTok, making it essential to analyze user interests and preferences effectively. In addition to algorithm development, you will also be tasked with addressing various e-commerce challenges, including the cold start problem and traffic allocation. This will involve creating long and short-term user interest models and utilizing machine learning technologies to improve the overall shopping experience. You will also have the opportunity to design and build debugging tools to support your work, ensuring that our systems run smoothly and efficiently.

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, model optimization and innovation.
• 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.
• 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 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.

Nice-to-haves

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

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.