Senior ML Engineer, E-commerce Recommendations

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

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