Staff Machine Learning Engineer (Ph.D. Graduate)

Job Description

Staff Machine Learning Engineer (Ph.D. Graduate) | TikTok

The Tone:
This is an early career position at TikTok, intended for Ph.D. graduates joining in 2026. The company’s Ads machine learning data platform team is crucial for building and maintaining the infrastructure that collects and processes vast amounts of data for machine learning training, serving, and privacy enforcement. This includes developing critical components like feature engineering, a robust feature store, and advanced training data generation systems. This role is vital because the quality and effectiveness of TikTok’s ads system, which supports millions of advertisers, clients, and influencers globally, directly depend on its ability to handle immense data volumes through sophisticated machine learning solutions.

The TL;DR
• Role: Early Career
• Type: Full-time
• Location: Location not specified in job description
• Pay: $136800–$259200 yearly
• Team: Ads machine learning data platform team
• Mission: Build highly efficient and stable infrastructure to collect and process data for machine learning training, serving, and privacy enforcement for TikTok’s Ads system.

What You’ll Actually Do
• Lead: Lead projects focused on building and operating scalable and reliable Ads ranking infrastructure systems.
• Guide: Provide technical leadership and guidance to team members and project peers.
• Communicate: Communicate cross-functionally with various teams, organizations, and both internal and external stakeholders to drive engineering efforts.
• Innovate: Lead innovation initiatives, introducing new ideas and technologies to the team.

The Must-Haves
• Background: Doctorate Degree. Career level: Early Career. Final year Ph.D. candidate or recent Ph.D. graduate in Computer Science or a related technical discipline.
• Experience: Experience in managing projects. This position is for individuals graduating in 2026, with onboarding expected by the end of 2026.
• Skills: Strong Computer Science fundamentals, including expertise in algorithms, data structures, and software design; excellent problem-solving skills; solid coding skills; strong understanding of algorithms, particularly causal inference, uplift models, and deep learning.
• Bonus: Experience within the Ads domain; experience with building large-scale ranking infrastructure.