Machine Learning Engineer - E-commerce Recommendation

$145,000 - $250,000/Yr

Tiktok - San Jose, CA

Full-time - Mid Level

San Jose, CA

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

About the position

TikTok is the leading destination for short-form mobile video, with a mission to inspire creativity and bring joy to over 1 billion users globally. As a Machine Learning Engineer in the TikTok Recommendation team, you will be part of a dynamic group focused on enhancing the e-commerce experience through innovative machine learning algorithms. This role involves developing large-scale recommendation systems that cater to various aspects of e-commerce, including commodity recommendations, live stream recommendations, and short video recommendations. You will work on building models that analyze user interests and behaviors, leveraging vast amounts of data to create personalized experiences for users. In this position, you will participate in the design and implementation of algorithms that can handle millions of users and products. Your responsibilities will include building both long-term and short-term user interest models, extracting relevant information from diverse datasets, and designing algorithms that efficiently explore users' latent interests. You will also be tasked with developing predictive models for candidate generation and ranking, focusing on metrics such as Click Through Rate and Conversion Rate. This will involve creating real-time data pipelines, performing feature engineering, and optimizing models to ensure they deliver high-quality recommendations. As part of the team, you will also design and build supporting tools for debugging and enhancing the performance of the recommendation systems. The role requires a strong foundation in applied machine learning, programming skills, and a passion for solving real-world problems in the e-commerce domain. TikTok values creativity and innovation, and you will have the opportunity to contribute to impactful solutions that enhance user engagement and satisfaction.

Responsibilities

• Participate in building large-scale e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, and short video recommendations.
• 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, develop, evaluate, and iterate on predictive models for candidate generation and ranking, including building real-time data pipelines, feature engineering, model optimization, and innovation.
• Design and build supporting/debugging tools as needed.

Requirements

• Bachelor or above degree in Computer Science or related fields.
• Strong programming and problem-solving ability.
• Experience in applied machine learning, familiar with algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Gradient Boosting Trees, Deep Neural Networks, 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

• Experience in recommendation systems, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.

Benefits

• 100% premium coverage for employee medical insurance, approximately 75% premium coverage for dependents, and a 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 for 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.