Clark Zinzow
Senior Engineering Manager - Together Cloud at Together AI
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolClark Zinzow is a product-focused engineering leader with 11 years of experience building distributed systems and AI infrastructure, currently leading Together Cloud as Senior Engineering Manager from San Francisco. He’s a founding engineer who scaled a team from zero to 23 SWE and guided the GA launch of Instant Clusters—an AI hyperscaler spanning thousands of GPUs across dozens of data centers. Clark’s hands-on background includes core contributions to Ray (the AI compute engine used by major players) and designing high-performance distributed query and execution engines at Eventual, delivering multi-modal data support and large speedups for cloud storage reads and TPC-H workloads. He blends technical depth in decentralized scheduling, locality-aware sharding, and autoscaling actor pools with product ownership and program management. A dual math and computer science graduate from UW–Madison, he’s comfortable moving between low-level runtime fixes and high-impact product launches.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science (B.Sc.), Mathematics, Bachelor of Science (B.Sc.), Mathematics at University of Wisconsin-Madison
French, Spanish, Chinese, English
Stackoverflow
473reputation
118kreached
1answer
2questions
Github Skills (13)
ray10
back-end-development10
python10
debug9
debugging9
networking8
wordpress6
linux6
php6
bash6
grep6
visual-studio6
regex6
Programming languages (7)
Github contributions (5)
ray-project/ray
Mar 2020 - 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:2266 reviews, 281 commits, 546 PRs in 2 years 10 months
Contributions summary:Clark primarily focused on improving and maintaining the Ray core back-end codebase. Their contributions included removing duplicate dependencies from setup files, adding functionality for specifying different IP addresses for core workers and their raylets, and fixing Ray service startup when logging redirection is disabled. These modifications indicate expertise in Ray's internal workings.
pythonconsistsruntimetensorflowserving
ray-project/ray_shuffling_data_loader
May 2021 - Jan 2023
A Ray-based data loader with per-epoch shuffling and configurable pipelining, for shuffling and loading training data for distributed training of machine learning models.
Contributions:12 reviews, 28 commits, 6 PRs in 1 year 8 months
epochpipeliningrayconfigurableshuffling
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