Applied Machine Learning Engineer - USDS
TikTok.com
Office
San Jose, California, United States
Full Time
About the Team
You'll be an integral part of the USDS Cyber Defense & Engineering team, responsible for enhancing security tools and identifying vulnerabilities, with a specific focus on content assurance and the application of large language models (LLMs). You'll collaborate cross-functionally with partners inside and outside TikTok to fortify our products and users' security, helping to establish TikTok as the most trusted platform.
We are seeking a versatile, forward-thinking, and outcome-driven Content Assurance Specialist to propel our projects forward. In this capacity, you will engage with diverse technical and non-technical teams across various regions, contributing to the development of innovative, AI-driven solutions to complex content moderation challenges. If you thrive in a dynamic environment and relish the opportunity to shape the strategic trajectory of a large global organization, this role offers an exciting prospect.
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.
About the Role
The ideal candidate will possess demonstrated problem-solving abilities, sound business acumen, and a track record of collaborating with multiple teams to successfully deliver projects. They should exhibit a genuine passion for safeguarding the security and privacy of our users, and a strong understanding of how to leverage cutting-edge technology, like LLMs, to achieve that goal.
Responsibilities
- Collaborate Across Teams: Work closely with data scientists, software engineers, machine learning engineers, and product managers to understand the recommendation engine.
- Deep Expertise in Recommender Systems: Leverage your expertise in machine learning and coding to gain an in-depth understanding of context-aware recommender systems.
- Understand Core System Components: Understanding of key modules in the recommender system, including recall, ranking, and reranking, ensuring high-quality, personalized recommendations at scale.
- End-to-End Ownership: In-depth understanding of the complete lifecycle of machine learning systems, from building and maintaining data pipelines and feature engineering, to training models and integrating them seamlessly into production environments.
- Ensure Security & Compliance: Work with cybersecurity teams to ensure that the recommender systems align with compliance standards and implement practices that enhance user trust and experience.
- Support Automation & Prototyping: Contribute to quick prototyping and proof-of-concept initiatives that automate rule reviews within the recommendation systems, ensuring both efficiency and compliance.
- Document & Ensure Accessibility: Build and maintain comprehensive documentation for data processes and machine learning models, ensuring transparency, accessibility, and consistency across teams.
You'll be an integral part of the USDS Cyber Defense & Engineering team, responsible for enhancing security tools and identifying vulnerabilities, with a specific focus on content assurance and the application of large language models (LLMs). You'll collaborate cross-functionally with partners inside and outside TikTok to fortify our products and users' security, helping to establish TikTok as the most trusted platform.
We are seeking a versatile, forward-thinking, and outcome-driven Content Assurance Specialist to propel our projects forward. In this capacity, you will engage with diverse technical and non-technical teams across various regions, contributing to the development of innovative, AI-driven solutions to complex content moderation challenges. If you thrive in a dynamic environment and relish the opportunity to shape the strategic trajectory of a large global organization, this role offers an exciting prospect.
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.
About the Role
The ideal candidate will possess demonstrated problem-solving abilities, sound business acumen, and a track record of collaborating with multiple teams to successfully deliver projects. They should exhibit a genuine passion for safeguarding the security and privacy of our users, and a strong understanding of how to leverage cutting-edge technology, like LLMs, to achieve that goal.
Responsibilities
- Collaborate Across Teams: Work closely with data scientists, software engineers, machine learning engineers, and product managers to understand the recommendation engine.
- Deep Expertise in Recommender Systems: Leverage your expertise in machine learning and coding to gain an in-depth understanding of context-aware recommender systems.
- Understand Core System Components: Understanding of key modules in the recommender system, including recall, ranking, and reranking, ensuring high-quality, personalized recommendations at scale.
- End-to-End Ownership: In-depth understanding of the complete lifecycle of machine learning systems, from building and maintaining data pipelines and feature engineering, to training models and integrating them seamlessly into production environments.
- Ensure Security & Compliance: Work with cybersecurity teams to ensure that the recommender systems align with compliance standards and implement practices that enhance user trust and experience.
- Support Automation & Prototyping: Contribute to quick prototyping and proof-of-concept initiatives that automate rule reviews within the recommendation systems, ensuring both efficiency and compliance.
- Document & Ensure Accessibility: Build and maintain comprehensive documentation for data processes and machine learning models, ensuring transparency, accessibility, and consistency across teams.
Applied Machine Learning Engineer - USDS
Office
San Jose, California, United States
Full Time
September 13, 2025