Associate Machine Learning Engineer, Recommendations

In this role, you will help build and operate machine learning systems that power large-scale feed and e-commerce recommendations. TikTok USDS JV brings together applied machine learning engineers and data scientists focused on turning algorithms into production impact for hyper-scale experiences. The position is based in San Jose, CA (onsite).

What you will do

• Design, build, and maintain robust MLOps pipelines, including CI/CD for ML, to enable reliable continuous training, automated evaluation, and smooth deployment for recommendation models supporting 10M–100M+ users.
• Architect high-throughput real-time streaming data pipelines and feature stores, including offline and online feature synchronization for short-term and long-term user interest modeling.
• Optimize candidate generation and ranking models for production serving, including predictive approaches tied to CTR and CVR.
• Improve inference performance through techniques such as model quantization, parallel computation, and latency reduction, using tools like C++/CUDA/TensorRT or similar frameworks.
• Create automated monitoring, debugging, and visualization to track feature drift, model performance degradation, data leakage, and GPU/CPU cluster utilization.
• Collaborate with algorithm researchers and platform teams to move large-scale deep learning models from experimental prototypes to zero-downtime production deployment.

Required qualifications

• Bachelor’s degree or higher in Computer Science or a related field.
• Strong programming and problem-solving skills.
• Basic familiarity with MLOps concepts, including data preprocessing, Docker containerization, model tracking/versioning, and workflow orchestration.
• Motivation to tackle real-world distributed systems and machine learning operational challenges.

Technologies

• MLOps, CI/CD, Docker
• C++, CUDA, TensorRT
• CTR, CVR

About the team

You will join a group of applied machine learning engineers and data scientists working on general feed recommendations and e-commerce recommendations. The team develops innovative algorithms and techniques to improve user engagement and satisfaction, and applies large-scale machine learning to real-world problems. The organization is shifting from a hybrid work model to a fully in-person schedule up to 5 days a week.

Preferred qualifications

• Experience in recommendation systems, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related areas.

Compensation

The salary range for this position is USD 122,574 - 259,200 per year.

Benefits

• Day one access to medical, dental, and vision insurance
• 401(k) savings plan with company match
• Paid parental leave
• Short-term and long-term disability coverage
• Life insurance
• Wellbeing benefits
• 10 paid holidays per year
• 10 paid sick days per year
• 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)
• Eligible for additional discretionary bonuses/incentives and restricted stock units

About USDS

TikTok USDS Joint Venture LLC focuses on the safety and security of millions of Americans who create, discover, and connect on the apps operated by the company. The Joint Venture was established in compliance with the Executive Order signed by President Trump on September 25, 2025. USDS operates a comprehensive data privacy and cybersecurity program under defined safeguards to protect national security and secure U.S. user data, apps, and the algorithm. It also safeguards the U.S. content ecosystem through trust and safety decision-making authority for policies and moderation, and supports Americans in expressing creativity, discovering new hobbies and interests, and building thriving communities and businesses.

Reasonable accommodation

USDS provides reasonable accommodations in recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other reasons protected by applicable laws. If you need assistance, contact https://tinyurl.com/USDS-RA.