Responsibilities
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok’s global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us
Creation is the core of TikTok’s purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible.
Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day.
To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
At TikTok, we create together and grow together. That’s how we drive impact - for ourselves, our company, and the communities we serve.
Join us.
About the team
The Applied AI (DCC) Team is part of the Monetizing Integrity team in TikTok global business solutions. We support Data Cycling Center to provide enterprises with affordable and trusted data and models.

PREFERRED QUALIFICATIONS:

1. Experience with any Go/Python Microservices framework.
   TikTok is 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. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
   

   LI-SP

1. Architect and implement ML systems that are scalable, repeatable, and secure, to support team’s need for complex business AI applications;
2. Consult with business stakeholders to understand and evaluate current data analytics and machine learning needs;
3. Document machine learning processes, models, and data dictionaries, communicating technical concepts in a clear and concise manner to a variety of audiences;
   Specifically, the ML system should support the following features:
4. Model optimization: collaborate with data scientists to improve existing machine learning model training and evaluation pipelines, optimize the model training pipeline speed for faster iteration;
5. Model Deployment: optimize the model inferencing performance through quantization and model conversion, define and leverage appropriate resources for model hosting and inferencing;
6. Inference Pipeline product prioritization: work with data scientists and data engineers to design and implement the data pipelines for machine learning models that will support the current and future needs of our business;
7. Service Deployment: build continuous integration, testing, and scalable deployment pipelines in cloud computing environments for machine learning services;
8. Tracking: build logging, tracking, analyzing, monitoring, and reporting pipelines for both data and model tracking in cloud computing environments to ensure correct model output and stable model performance;
9. Maintenance: build scalable and reliable infrastructure that supports feature engineering, model training, deployment, inferencing, performance monitoring.
   Qualifications
10. BS or above in Computer Science, Software Engineering, Data Science, or a related field;
11. 5 years of industry experience building ML infrastructure at scale, 2 years of experience in developing and deploying large-scale systems, version control, scaling and monitoring;
12. Experience in Machine Learning frameworks (scikit-learn, Tensorflow, Pytorch), big data frameworks (Spark/Hadoop/Flink), and experience in resource management and task scheduling for large-scale distributed systems;
13. Proficient in Python/SQL and of C++/Go, with deep knowledge of Linux and CD tools (e.g. Git);
14. Familiar with cloud infrastructure, good understanding of different data storages and message queues for data streaming and pipelining;
15. Good communication and teamwork skills to communicate technical concepts with other teammates.