Philipp Moritz
Co-founder And CTO at Anyscale
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolPhilipp Moritz is a co-founder and CTO based in Berkeley with 16 years of experience building high-performance distributed systems and ML infrastructure. He combines a formal academic background—PhD in Computer Science from UC Berkeley and advanced study in mathematics at Cambridge—with hands-on engineering across projects like Apache Arrow and Ray, contributing performance-critical C++ and memory-management work. At Anyscale he translates research-grade distributed runtimes into production-ready platforms, focusing on object stores, serialization, and efficient data transfer for large-scale ML. His open-source work also spans LLM inference optimizations in vLLM and integrating deep learning into distributed data frameworks such as SparkNet, showing comfort across low-level systems and model-serving stacks. Known for squeezing latency and memory overhead out of complex pipelines, he often implements the gritty plumbing—serializers, custom allocators, and fused kernels—that makes scalable ML practical. He brings a blend of academic rigor and startup pragmatism that accelerates tooling from prototype to production.
16 years of coding experience
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of California, BerkeleyBachelor of Science (BS), Physics, Bachelor of Science (BS), Physics at Julius-Maximilians-Universität WürzburgMaster of Advanced Study, Mathematics, Master of Advanced Study, Mathematics at University of Cambridge
Github Skills (51)
pytorch10
c-language10
caffe10
python10
testing10
memory-management10
machine-learning10
data-serialization10
object-storage10
cmake10
inference10
arrow-keys10
data-structure10
cicd10
reinforcement-learning10
Programming languages (18)
Github contributions (5)
amplab/SparkNet
Nov 2015 - Apr 2016
Distributed Neural Networks for Spark
Role in this project:
ML Engineer
ML EngineerContributions:135 commits, 55 PRs, 156 pushes in 5 months
Contributions summary:Philipp's contributions primarily focused on integrating and extending the Caffe deep learning framework within the SparkNet project. They added a Java data layer, which enables the loading of data from Java, likely facilitating the integration of Spark RDDs as input to the neural networks. The user also implemented tests for Cifar10 datasets, expanded the Caffe library and provided image preprocessing capabilities. Furthermore, the user contributed to the creation of a C wrapper for Caffe.
neural-networksmachine-learningsparkscaladistributed
ray-project/tutorial
Jul 2017 - Oct 2019
Role in this project:ML Engineer
ML EngineerContributions:13 commits, 63 PRs, 48 pushes in 2 years 2 months
Contributions summary:Philipp implemented a policy gradient algorithm for training a CartPole environment, indicating a focus on reinforcement learning. The user's code modifications involve setting up and training a policy network using TensorFlow and the gym environment. They focused on implementing the core training loop, including action selection, reward calculation, and model updates. Additionally, the user worked on parallelizing the policy gradient implementation using Ray.
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