About The Team
The Multimedia Arch team is responsible for user experience optimization and cost optimization for the whole video on demand and live streaming system.
We are building intelligence and personalized end-to-end multimedia systems to power the whole Tiktok ecosystem. We are currently seeking a passionate machine learning engineer to join our team.
In this role, you will collaborate with the product and engineering teams to identify opportunities and improve overall system performance and efficiency by integrating AI algorithms across the entire multimedia system.
Your responsibilities will include designing service-side ML systems, implementing ML models, and validating their impact on the overall system through A / B testing.
Responsibilities : - Develop production machine learning solutions to understand user preferences and video contents, predict video popularity and the change of system resources to build a world class personalized multimedia experience.
Collaborate with engineering team and product team to shape the architecture roadmap applying ML.- Design and optimize the pipelines for server-side machine learning models, including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation.
Minimum Qualifications- 4 years of industry experience developing machine learning models with business impact, and shipping ML solutions to production.
- A master's or PhD degree in computer science, data science, or a ML related field.- Familiar with applied machine learning, such as classification, deep neural networks, transformers, multi-task learning, etc.
- Proficient in at least one programming language, such as Python and C++.- Experience in Deep Learning Tools such as tensorflow / pytorch.
- Have a passion for learning and an eagerness to experiment with new techniques and good communication and a cooperative spirit.
Preferred Qualifications- Experience with recommendation systems, online advertising, search, and causal inference is preferred.
Additional familiarity with LLM is a plus.
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