Machine Learning Engineer, Personalization & Scalable Systems

TikTok USDS Joint Venture; San Jose (CA); On-site; USD 137,000 - 259,000; Full time

Benefits offered by this job

Medical, Dental, Vision insurance

401(k) with company match

Paid parental leave

Disability insurance

Life insurance

Wellbeing benefits

10 paid holidays per year

10 paid sick days per year

17 days of Paid Personal Time

Job summary

TikTok USDS Joint Venture is seeking a PhD-level candidate to advance machine learning across recommendation, search, and e-commerce systems. You will design scalable ML solutions with strong data governance, privacy, and security for U.S. users.

Join a mission-driven team focused on delivering personalized experiences while maintaining world-class data protection. On-site work schedule supports rapid collaboration and impactful execution.

Qualifications

• PhD completion in relevant fields; strong fundamentals in data structures, algorithms, and software design.
• Solid understanding of machine learning concepts and theory.
• Experience or strong interest in ML applications at scale.

Responsibilities

• Design, build, train, and evaluate ML models for recommendation, search, and conversion prediction.
• Develop efficient pipelines for feature extraction, training, evaluation, and deployment with A/B testing.
• Process and analyze large-scale user data and logs to construct real-time/offline features.
• Optimize online inference latency and throughput for high-QPS services.
• Iterate on models based on online metrics to drive business goals.

Skills

PhD in CS/Engineering/Math/Stats

Data Structures & Algorithms

Machine Learning fundamentals

Education

PhD in CS/Engineering/Math/Stats

Job description

TikTok USDS Joint Venture is seeking a PhD-level candidate to advance machine learning across recommendation, search, and e-commerce systems. You will design scalable ML solutions with strong data governance, privacy, and security for U.S. users.

Join a mission-driven team focused on delivering personalized experiences while maintaining world-class data protection. On-site work schedule supports rapid collaboration and impactful execution.