Company Overview
TikTok is the premier platform for short-form video content, designed to inspire creativity and fuel imagination. This philosophy extends to the teams behind the platform, where innovation and curiosity drive success.
Our employees embrace a culture of exploration and adaptability, keeping pace with ever-evolving trends. With a flat organizational structure, TikTok provides dynamic opportunities for individuals to make a meaningful impact in a fast-growing global company. With offices spanning Asia Pacific, the Middle East, Europe, and the Americas, we are just getting started.
Responsibilities
-
Lead a dedicated engineering team in designing and implementing new features for TT4B recommendations.
-
Define and drive the overall roadmap for TT4B recommendations, ensuring continuous product excellence.
-
Develop, deploy, optimize, troubleshoot, and manage TT4B end-to-end (E2E) systems.
-
Collaborate with attribution and data engineering teams to monitor and enhance TT4B recommendation performance.
-
Work with leadership and TT4B sister teams to promote the TT4B recommendation framework and align recommendation systems with TT4B E2E ad solutions.
-
Partner with product managers to refine, review, and drive iterations of TT4B recommendation requirements.
Qualifications
Minimum Requirements:
-
Bachelor’s degree in Computer Science or a related technical field.
-
Hands-on experience in full-stack development (both backend and frontend).
-
Proven ability to lead and mentor team members.
-
Strong track record of driving large-scale cross-team collaborations.
-
Expertise in designing highly scalable and low-latency system architectures.
-
5+ years of experience in software engineering, with strong fundamentals in algorithms, data structures, and software design.
-
Proficiency in designing, implementing, and managing large-scale distributed systems.
-
Excellent teamwork skills, including communication, ownership, empathy, and integrity.
Preferred Qualifications:
-
Experience in the advertising industry.
-
Background in recommendation engines, machine learning, data analysis, domain-driven design, or complex infrastructure.