About this role
Full Time Software Engineer - Machine Learning Infrastructure - USDS in AI at TikTok in San Jose, California, United States. Apply directly through the link below.
At a glance
- Work mode
- Office
- Employment
- Full Time
- Location
- San Jose, California, United States
Core stack
- Machine Learning
- Infrastructure
- Mentoring
- Design
- ML
- AI
Quick answers
What skills are required?
Machine Learning, Infrastructure, Mentoring, Design, ML, AI.
TikTok is hiring for this role. Visit career page
San Jose, United States
About the team
The mission of our AML team is to push the next-generation AI infrastructure and recommendation platform for the ads ranking, search ranking, live & ecom ranking in our company. We also drive substantial impact on core businesses of the company. Currently, we are looking for Machine Learning Engineer - Machine Learning Infrastructure to join our team to support and advance that mission.
Responsibilities:
- Responsible for the design and implementation of a global-scale machine learning system for feeds, ads and search ranking models.
- Responsible for improving use-ability and flexibility of the machine learning infrastructure.
- Responsible for improving the workflow of model training and serving, data pipelines, storage system and resource management for multi-tenancy machine learning systems.
- Responsible for designing and developing key components of ML infrastructure and mentoring interns.
The mission of our AML team is to push the next-generation AI infrastructure and recommendation platform for the ads ranking, search ranking, live & ecom ranking in our company. We also drive substantial impact on core businesses of the company. Currently, we are looking for Machine Learning Engineer - Machine Learning Infrastructure to join our team to support and advance that mission.
Responsibilities:
- Responsible for the design and implementation of a global-scale machine learning system for feeds, ads and search ranking models.
- Responsible for improving use-ability and flexibility of the machine learning infrastructure.
- Responsible for improving the workflow of model training and serving, data pipelines, storage system and resource management for multi-tenancy machine learning systems.
- Responsible for designing and developing key components of ML infrastructure and mentoring interns.