Machine Learning Engineer - E-commerce Recommendation

TikTok; San Jose (CA); On-site; USD 162,000 - 317,000; Full time

Job summary

TikTok is seeking experienced ML engineers and data scientists to join the E-commerce recommendations team in San Jose. You will build large-scale recommender systems, including item, live-stream, and short-video recommendations, and develop real-time data pipelines and models for ranking and CTR/CR prediction.

You will work with researchers to translate creative ideas into scalable solutions, contribute to feature engineering, model optimization, and tooling, and collaborate across data

Qualifications

• Bachelor's degree or higher in Computer Science or related fields.
• Strong programming and problem-solving ability.
• Experience in applied machine learning with 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.

Responsibilities

• Participate in building large-scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity, live stream, and short video recommendations in TikTok.
• Build long and short term user interest models; analyze large data to design algorithms for latent interests.
• Design, develop, evaluate and iterate predictive models for candidate generation and ranking (e.g., CTR and CVR), including real-time data pipelines, feature engineering, model optimization and innovation.
• Design and build supporting/debugging tools as needed.

Skills

Programming

Applied ML

C++/Python

TensorFlow

PyTorch

Education

Bachelor's degree in CS

Tools

TensorFlow

PyTorch

C++

Python

Job description

Responsibilities

E-commerce is a new and fast growing business that aims at connecting all customers to excellent sellers and quality products on TikTok Shop, through E-commerce live-streaming, E-commerce short videos, and commodity recommendation. We are a group of applied machine learning engineers and data scientists that focus on E-commerce recommendations. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions. We are interested and excited in applying large scale machine learning to solve various real-world problems in E-commerce.

• Participate in building large-scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok.
• 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(eg. Click Through Rate and Conversion Rate prediction) , including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation.
• Design and build supporting/debugging tools as needed.

Qualifications

Minimum Qualifications

• Bachelor's degree or higher in Computer Science or related fields.
• Strong programming and problem-solving ability.
• 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.

Preferred Qualifications

• 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.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $162000 - $316800 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems;
3. Exercising sound judgment.