Ivan Gozali
Engineering Manager, Autopilot AI Infra at Tesla
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolIvan Gozali is an engineering manager leading Autopilot AI infrastructure at Tesla with 11 years of experience building large-scale backend and training systems. He progressed through individual contributor roles into management while driving distributed training, performance optimization, and AI networking for production ML workloads. Prior to Tesla he engineered data platforms at Lecida and contributed core execution and sampling improvements to the well-known DeepDive open-source project, reflecting a strong systems and research-infused background. Based in Berkeley and holding an MS in Computer Science from Stanford and a BS with high honors from UC Berkeley, he blends rigorous academic training with production-first delivery. Colleagues know him for tuning complex pipelines end-to-end and hiring for high-impact backend and ML infrastructure roles.
11 years of coding experience
10 years of employment as a software developer
Bachelor of Science (B.S.) Electrical Engineering and Computer Science with High Honors, Bachelor of Science (B.S.) Electrical Engineering and Computer Science with High Honors at University of California, BerkeleyHigh School Diploma Natural Sciences, High School Diploma Natural Sciences at SMA Kolese KanisiusMaster of Science (M.S.) Computer Science, Master of Science (M.S.) Computer Science at Stanford University
Indonesian, English, Chinese
Stackoverflow
2,189reputation
305kreached
10answers
8questions
etl
top-5%
Github Skills (12)
graph10
factors10
c-language10
cprogramming-language10
python9
etl9
machine-learning8
apache-spark6
permission6
logging6
conventions6
airflow6
Programming languages (14)
Github contributions (5)
HazyResearch/deepdive
Jan 2016 - May 2016
DeepDive
Role in this project:
Back-end Developer
Back-end DeveloperContributions:45 commits, 2 PRs, 24 pushes in 3 months
Contributions summary:Ivan primarily worked on refactoring and improving the DeepDive codebase. Their commits included the initial implementation of a compute-execute feature for Torque schedulers, suggesting work on the system's core execution. They also made several changes to improve the codebase, including a save weights snapshot feature, alongside modifications to files related to the Gibbs sampling algorithm and command parsing, indicating a focus on improving the functionality and stability of the DeepDive framework.
deep-learning
igozali/krs
Sep 2019 - Dec 2019
Simple Kafka command-line tool
Contributions:1 release, 35 commits, 4 PRs in 3 months
command-line-tooljqkafkacommand-linecli
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