TikTok

Machine Learning Engineer, Seller Growth - USDS

Mountain View, CA
SQL Python Hadoop Spark Deep Learning PyTorch Machine Learning
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Description
TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. U.S. Data Security (“USDS”) is a subsidiary of TikTok in the U.S. This new, security-first division was created to bring heightened focus and governance to our data protection policies and content assurance protocols to keep U.S. users safe. Our focus is on providing oversight and protection of the TikTok platform and U.S. user data, so millions of Americans can continue turning to TikTok to learn something new, earn a living, express themselves creatively, or be entertained. The teams within USDS that deliver on this commitment daily span across Trust & Safety, Security & Privacy, Engineering, User & Product Ops, Corporate Functions and more.

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
TikTok is building a global e-commerce platform that connects millions of users with their favorite brands and products. Our Supply Chain and Logistics team plays a critical role in ensuring smooth operations and customer satisfaction. The team is dedicated to enhancing the shopping experience and reducing operational costs in the supply chain and logistics ecosystem by developing end-to-end algorithm capabilities using machine learning, operations research, data mining, and causal inference methods.

The Supply Chain and Logistics - Seller Growth team is responsible for
- Leading efforts in merchant and creator acquisition, fostering the growth of the platform.
- Implementing incubation programs and incentives to support the development of merchants and creators.
- Utilizing data-driven approaches for effective merchant and creator matching, enhancing collaboration and content creation.

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.

As a Machine Learning Engineer, you will contribute to the development and implementation of cutting-edge machine learning models and algorithms to optimize seller growth.

Responsibilities:
- Streamline Data Workflows. Build scalable data pipelines for automated preprocessing, model training, and prediction. Conduct data processing and exploratory analysis for large-scale datasets.
- Innovative Machine Learning Solutions. Design, develop, and optimize advanced machine learning algorithms for diverse tasks. Monitor model performance, identify anomalies, and proactively address potential issues.
- Cross Functional Collaborations. Collaborate with data scientists, analysts, and subject matter experts to define objectives and develop strategies for complex business challenges. Collaborate with software engineers to integrate machine learning models into production systems. Communicate findings, insights, and technical concepts adeptly to both technical and non-technical stakeholders.
- Continuous Learning. Stay abreast of the latest advancements in data mining, machine learning, and related fields. Apply emerging knowledge to enhance team capabilities and identify new opportunities.Minimum Requirements:
- Hold a Master's or advanced degree in Computer Science, Data Science, Statistics, Operations Research or a related field.
- Possess 2+ years of experience as a Machine Learning Engineer or Data Scientist.
- Proficient in using SQL and Python, with expertise in data manipulation and analysis libraries.
- Familiarity with big data processing frameworks such as Hadoop and Spark. Demonstrated experience in distributed computing for efficient data mining on large-scale datasets.
- Possess a solid understanding of machine/deep learning concepts and techniques, including feature engineering, model evaluation, and optimization.
- Exhibit strong analytical and problem-solving skills, showcasing the ability to derive insights from complex and unstructured datasets.
- Excellent communication and collaboration skills. Proven ability to work effectively in cross-functional teams and articulate technical concepts to non-technical stakeholders.

Preferred Qualifications:
- Prior experience in Deep Learning.
- Experience with PyTorch, demonstrating a strong foundation in machine learning frameworks.

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.

TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://shorturl.at/ktJP6

This role requires the ability to work with and support systems designed to protect sensitive data and information. As such, this role will be subject to strict national security-related screening.
TikTok
TikTok
Content Creators Content Discovery Mobile Social Media Video

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