Associate Machine Learning Engineer, Recommendations

About the team

We are a group of applied machine learning engineers and data scientists that focus on general feed recommendations and E-commerce recommendations. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions. We are interested and excited in applying large scale machine learning to solve various real-world problems.

What you will do

• Participate in designing, building, and maintaining robust MLOps pipelines (CI/CD for ML) for hyper-scale (10M–100M+ users) recommendation models, ensuring reliable continuous training, automated evaluation, and seamless deployment.
• Architect high-throughput real-time streaming data pipelines and feature stores (offline & online feature synchronization) to support dynamic user interest modeling (short-term & long-term signals).
• Optimize candidate generation and ranking predictive models (CTR, CVR) for production serving. Work on inference acceleration, model quantization, parallel computation, and latency reduction using C++/CUDA/TensorRT or similar frameworks.
• Build automated monitoring, debugging, and visualization tools for tracking feature drift, model performance degradation, data leakage, and GPU/CPU cluster utilization.
• Partner with algorithm researchers and platform teams to streamline the lifecycle of large-scale deep learning models from experimental prototype to zero-downtime production deployment.

Minimum Qualifications

• Bachelor's degree or higher in Computer Science or related fields.
• Strong programming and problem-solving ability.
• Basic understanding of MLOps principles, including data preprocessing, containerization (Docker), model tracking/versioning, or workflow orchestration.
• Driven to solve complex real-world distributed systems and machine learning operational challenges.

Preferred Qualifications

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