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# Associate Machine Learning Engineer, Recommendations

- **Company:** Tiktok Usds
- **Location:** San Jose, CA, United States
- **Salary:** $122,574.0 - $259,200.0
- **Contract:** Permanent contract
- **Skills:** Big Data, C++ (Programming Language), Nvidia CUDA, Computer Programming, Continuous Integration, Data Cleansing, Information Leak Prevention, Data Visualization, Software Debugging, Distributed Systems, Information Retrieval, Machine Learning, Natural Language Processing, Recommender Systems, Management of Software Versions, Feature Store, Deep Learning, Parallel Computation, Containerization, Information Technology, Low Latency, Model Quantization, Machine Learning Operations, TensorRT, Data Pipelines, Docker
- **Published:** October 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f267241deb14062e

## About the Role

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.

## Description

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.