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Engineering Manager, Recommendations - USDS
The TikTok Recommendation team sits in the center of TikTok, designs, implements and improves the recommendation algorithm that powers the TikTok For You Page (FYP).
The team is at the intersection of cutting-edge machine learning research and large-scale end-to-end production systems. We take pride in finding the right balance between solid applied research, elegant system design and being pragmatic. We have a strong user focus and a dedication to technical excellence.
In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time.
What you'll do
- Lead the team to improve recommendation models at massive scale, through applying state-of-the-art machine learning techniques across all ranking phases including but not limited to retrieval, ranking, re-ranking, etc.
- Drive team to apply cutting-edge application-driven research to explore the frontier of recommendation algorithmic domain. Drive team to develop industry leading recommendation systems.
- Drive team to cross functionally work with product managers, data scientists and product engineers to understand insights, formulate problems, design and refine machine learning algorithms, and communicate results to peers and leaders.
- Have a good understanding of end-to-end machine learning systems. Work with infra teams to improve efficiency and stability.
The team is at the intersection of cutting-edge machine learning research and large-scale end-to-end production systems. We take pride in finding the right balance between solid applied research, elegant system design and being pragmatic. We have a strong user focus and a dedication to technical excellence.
In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time.
What you'll do
- Lead the team to improve recommendation models at massive scale, through applying state-of-the-art machine learning techniques across all ranking phases including but not limited to retrieval, ranking, re-ranking, etc.
- Drive team to apply cutting-edge application-driven research to explore the frontier of recommendation algorithmic domain. Drive team to develop industry leading recommendation systems.
- Drive team to cross functionally work with product managers, data scientists and product engineers to understand insights, formulate problems, design and refine machine learning algorithms, and communicate results to peers and leaders.
- Have a good understanding of end-to-end machine learning systems. Work with infra teams to improve efficiency and stability.