Amanda Liu
Software Engineer at University of Maryland
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAmanda Liu is a software engineer at Databricks with five years of experience building reliable backend systems and ML-enabled tooling, currently contributing to Apache Spark. She has a strong open-source footprint—improving Spark’s error handling and test utilities—and has implemented advanced quantization methods in PyTorch for model optimization. Amanda blends production engineering with research experience in NLP/HCI, having developed a PySpark test framework, English-to-Spark SDK features, and an AI search engagement prototype. She also mentors and teaches extensively (managing large CS courses and interview prep), highlighting a talent for translating complex technical concepts into practical learning. Based in Mountain View, she pairs rigor from a summa cum laude CS degree with a track record of measurable impact in large-scale data and ML systems.
5 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS Computer Science (Honors Thesis) summa cum laude, Bachelor of Science - BS Computer Science (Honors Thesis) summa cum laude at University of MarylandScience Mathematics and Computer Science Magnet Program , Science Mathematics and Computer Science Magnet Program at Montgomery Blair High School
English, Chinese, Spanish
Github Skills (19)
pytorch10
testing10
dataframes10
machine-learning10
pyspark10
dataframe10
scala10
deep-learning10
quantization10
error-handling10
tensor10
test-automation10
python9
autograd9
javas8
Programming languages (6)
Github contributions (5)
pytorch/pytorch
May 2022 - Aug 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
ML EngineerContributions:55 reviews, 505 commits, 79 PRs in 2 months
Contributions summary:Amanda's contributions focused on implementing and refining APoT (Approximate Positive or Zero Tensor) quantization methods within the PyTorch framework. This involved creating classes for APoT tensors and quantizers, implementing quantization and dequantization methods, and developing supporting utility functions for conversions between floating-point and APoT representations. Additionally, the user added unit tests to validate the correctness of the APoT quantization process and evaluate its performance, including the implementation of fake quantization for quantization-aware training.
pythongpu-accelerationdeep-learninggpunumpy
apache/spark
May 2023 - Mar 2025
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:Backend Developer & Test Automation Engineer
Backend Developer & Test Automation EngineerContributions:58 reviews, 73 PRs, 72 comments in 1 year 10 months
Contributions summary:Amanda's contributions focused on improving the error handling and testing capabilities of the Apache Spark project. Their work included assigning more descriptive names to error classes and modifying and expanding the testing utilities, specifically the `assertDataFrameEqual` function. They also implemented support for comparing lists of Rows and pandas DataFrames, and refined the formatting of error messages for better readability. Furthermore, the user was involved in refactoring the test suite to leverage the new test utilities and adding new test cases, demonstrating a commitment to code quality and test coverage.
analyticspythondata-processingsqlapache
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