Machine Learning Engineer, TikTok Ads Core- Traffic
2 days ago
TikTok is a leading short-form mobile video platform that inspires creativity and brings joy. Our mission is to help imaginations thrive, and we're looking for innovative Machine Learning Engineers to join our TikTok Ads Core team.
We're committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe, and so does our workplace. We aim to drive impact through collaboration, innovation, and growth.
Responsibilities
We're seeking highly skilled Machine Learning Engineers to build scalable systems and models that improve ads ranking results. You will be part of a team that optimizes ads format and ranking strategies, responsible for bringing a better return on investment for advertisers.
You will explore new features to improve model accuracy, understand ads platform objectives, and take full advantage of modern machine learning to enhance ad quality, relevancy, and select the best ad formats delivered to end-users. Additionally, you will develop robust core systems and algorithms, including ads format selection system, ads ranking system, model serving system, etc.
Estimated Salary: $160,000 - $220,000 per year
Requirements
To succeed in this role, you'll need a BS/MS degree in Computer Science, Computer Engineering, or a related technical discipline with 3+ years industry experience and 2+ year model optimization experience. You should have solid programming skills, proficient in C/C++ and Python, familiar with basic data structure and algorithms, and Linux development environment. Good analytical thinking capability, essential knowledge, and skills in statistics, as well as good theoretical grounding in deep learning concepts and techniques are also required.
Familiarity with architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/Pytorch/MXNet) is preferred. Experience in resource management and task scheduling with large scale distributed software (such as Spark and TensorFlow) is also desirable.
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