Tech Lead, Machine Learning E-commerce Feed Recommendation

$212,800 - $389,500/Yr

Tiktok - Seattle, WA

Full-time - Mid Level

Seattle, WA

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

About the position

As a Tech Lead for Machine Learning in E-commerce Feed Recommendation at TikTok, you will play a pivotal role in shaping the future of our interest-based E-commerce platform. This position is centered around developing and enhancing large-scale recommendation algorithms that cater to millions of users engaging with live-streaming and short video content on TikTok. Your work will directly impact how users discover products and connect with sellers, creating a personalized shopping experience that is unique to TikTok. You will be part of a dynamic team of applied machine learning engineers and research scientists dedicated to innovating algorithms that improve user engagement and satisfaction. The recommendation system is crucial in helping customers explore their shopping interests, and your expertise will be instrumental in driving this initiative forward. You will be tasked with designing, developing, and iterating on predictive models that enhance candidate generation and ranking, ensuring that our algorithms are not only effective but also scalable to handle the vast amounts of data generated daily. In this role, you will also focus on building long and short-term user interest models, analyzing large datasets to extract relevant information, and designing algorithms that efficiently explore users' latent interests. You will tackle various e-commerce challenges, such as the cold start problem and traffic allocation, using machine learning technologies to improve the overall shopping experience. Additionally, you will be responsible for creating supporting tools for debugging and enhancing the performance of our systems.

Responsibilities

• Participate in building large-scale (10 million to 100 million) 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.
• 5 years 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

• 3 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, 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 like Health Care, Limited Purpose and Dependent Care.
• 10 paid holidays per year plus 17 days of Paid Personal Time Off (PPTO) (prorated upon hire and increased by tenure) 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 our EAP and Lyra.
• 401K company match, gym and cellphone service reimbursements.