Machine Learning Engineer - Measurement Signal and Privacy Technology

关于这个机会

Our Ads Measurement Signal and Privacy team (MSP) is missioned to build our security and governance infrastructure and process for the ads system end-to-end while making sure our platform with large volume of various signals and data are constantly compliant with regional regulations across the world. The team will be responsible for exploring and implementing cutting-edge privacy-enhancing technologies to protect our users while keeping TikTok a free and creative community. You will be a key player in data privacy, protection, security and managing cross-functional programs to continuously improve the process. Responsibilities: By joining us, you'll have the opportunity to participate in the development of algorithms and systems for one of the world's leading ranking and recommendation services. Your work will mainly include: - Developing advanced ML and LLM algorithms (including retrieval, ranking, calibration modules) for signal matching systems at the scale of millions of QPS - Iterating and optimizing ranking algorithms and strategies through data analysis and experimentation. - Leading the development and operational work of large-scale, distributed real-time systems for testing, integrating, and deploying machine learning models. - Creating algorithms and data-driven solutions that utilize one of the world’s richest user behavior datasets for various ads-related use cases, such as ads ranking, targeting, bidding, calibration, and reporting. - Collaborating with product teams to define product strategies and features.

任职要求

Minimum Qualifications: - Bachelor's or Master's degree in Computer Science, Computer Engineering, or related fields - Proficient in one or more general-purpose programming languages, including but not limited to: Go, C/C++, Python. - Experience in machine learning/deep learning algorithm development at large internet companies in one of the following areas: search, ads, or recommendation. Preferred Qualifications： - PhD and research experience in AI fields such as ML, NLP, CV. - Experience of engineering development experience for systems at millions of QPS scale - Experience leading complex projects with successful delivery.

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