Ying Chen
Software Engineer at Databricks
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYing Chen is a software engineer with a decade of experience building ML infrastructure and inference runtimes, currently focusing on production inference at Databricks in San Francisco. Trained at Tsinghua (BE) and Carnegie Mellon (MS in Intelligent Information Systems), she combines strong systems engineering with deep ML tooling knowledge. Her open-source contributions to MLflow improved compatibility with LLMs by adding OpenAI-style APIs, embeddings for sentence transformers, and support for chat/completion workflows—work that eases deploying text-generation models in production. Ying has hands-on experience across both training infra and inference, enabling her to bridge model development and scalable deployment. Colleagues describe her as pragmatic and detail-oriented, with a knack for making complex ML tooling more usable for engineers and data scientists.
10 years of coding experience
Bachelor of Engineering - BE, Computer Science, Bachelor of Engineering - BE, Computer Science at Tsinghua UniversityMaster of Science - MS, Intelligent Information Systems, Master of Science - MS, Intelligent Information Systems at Carnegie Mellon University
Github Skills (11)
transformers10
machine-learning10
nlp10
mlflow10
python10
sentence-transformers10
api-design9
model-management9
openai-api8
ml8
ai8
Programming languages (7)
Github contributions (5)
mlflow/mlflow
Jan 2022 - Mar 2024
Open source platform for the machine learning lifecycle
Role in this project:
ML Engineer
ML EngineerContributions:18 reviews, 5 PRs, 23 comments in 2 years 2 months
Contributions summary:Ying primarily contributed to enhancing the MLflow library, particularly in the areas of text-generation models and sentence transformers. They added support for OpenAI-compatible API interfaces for models like sentence transformers, enabling embeddings functionality. Furthermore, they modified the transformers' codebase to accommodate chat and completions tasks, and integrated signatures. These modifications improve the usability and compatibility of MLflow with LLM models and OpenAI standards.
pythonlifecyclemlmachine-learningincremental-learning
databricks/databricks-ml-examples
Jun 2023 - Mar 2024
Contributions:80 reviews, 47 PRs, 79 pushes in 9 months
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