Eric Liang
MLE Intern at Stanford Artificial Intelligence Laboratory (SAIL)
United States
Summary
🤩
Rockstar🎓
Top SchoolEric Liang is a machine-learning engineer and researcher with 16 years of software experience who blends hands‑on systems work with robotics and generative modeling research. Currently an MLE intern at Waymo and a researcher at Stanford SAIL, he has tackled real-to-sim-to-real manipulation, action chunking for VLA models, and synthetic-data pipelines. His background includes performance-critical C++ contributions to the Ray distributed AI runtime and building a custom exoplanet classifier at NASA using autoencoders and classical ML. Eric also ships full-stack solutions and tooling—reducing developer workflows by 96% at Amazon and cutting pedestrian detection hardware costs dramatically in academic work—demonstrating a rare mix of low-level systems craft and applied ML. Notably, he pairs strong research advising (Chelsea Finn) with production-minded engineering across both open-source infrastructure and robotics.
16 years of coding experience
2 years of employment as a software developer
Branham High SchoolBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Stanford University
Github Skills (8)
memory-management10
debugging10
debug10
c-language10
cprogramming-language10
object-store10
concurrency10
ray9
Programming languages (10)
Github contributions (5)
ray-project/ray
May 2017 - Jan 2023
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 releases, 5822 reviews, 1279 commits in 5 years 9 months
Contributions summary:Eric's contributions focused on improving the performance and reliability of the Ray object store, specifically addressing race conditions and segment faults in the object buffer pool. They primarily interacted with C++ code related to low-level memory management and object retrieval, including changes to object buffer pool logic and structure. Their work involved directly modifying core infrastructure components within the Ray project.
pythonconsistsruntimetensorflowserving
ericl/spark
Jun 2015 - May 2019
Mirror of Apache Spark
Contributions:443 pushes, 129 branches in 3 years 11 months
spark-mlapachebig-datasparkscala
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