ML Engineer - Personalization & Growth Algorithms

TikTok; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Job summary

TikTok is seeking PhD-level researchers to join the e-Commerce Recommendation and Marketing Algorithm team in California. You will work on big data, risk-control and governance algorithms, and contribute to intelligent customer service technologies to boost user growth and GMV.

Responsibilities include optimizing core algorithms for recommendations and marketing, building value models, and applying uplift modeling and operations research techniques to improve efficiency.

Qualifications

• PhD in Computer Science, Electrical Engineering or related field with experience in C++ and Python.
• Strong algorithms and data structures foundations with excellent coding skills.
• Collaborative, fast learner with good communication and teamwork.

Responsibilities

• Participate in optimizing TikTok e-Commerce growth and marketing algorithms, including core capabilities such as user value modelling, personalized messaging, recommendations, and intelligent marketing.
• Establish a user lifecycle data and value system to address growth challenges for TikTok Mall users.
• Contribute to product and technical solutions for growth engines, like personalized push notifications/emails and recommendations.
• Optimize recall/sorting algorithms for new user recommendations to improve relevance and diversity.
• Improve marketing efficiency using uplift models and operations research methods.

Skills

C++

Python

Big Data

Deep Learning

Algorithms & DS

Communication

Education

PhD in CS/EE or related field

Tools

Hive SQL

Spark

MapReduce

TensorFlow

PyTorch

Job description

TikTok is seeking PhD-level researchers to join the e-Commerce Recommendation and Marketing Algorithm team in California. You will work on big data, risk-control and governance algorithms, and contribute to intelligent customer service technologies to boost user growth and GMV.

Responsibilities include optimizing core algorithms for recommendations and marketing, building value models, and applying uplift modeling and operations research techniques to improve efficiency.