Eli Sakov
Founder at Aurora
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolEli Sakov is a founder and seasoned software engineer with 12 years of experience building production ML and full‑stack systems from early-stage startups to self-driving perception teams in San Francisco. He blends hands-on model work—contributing performance improvements to Google Brain’s AutoML EfficientDet implementation—with product leadership, having led engineering and product for consumer and enterprise apps at unspun, Ondaka, and Kaliber Labs. As CTO and founding engineer he’s shipped web3 lending primitives, AR/VR enterprise demos, and a 4.9‑star body‑scanning retail app, demonstrating an uncommon mix of ML, cloud architecture, and delightful UX. He currently advises startups while founding Benefitsleads.com, and is known for turning customer conversations into concrete features and for training models on synthetic 3D data to solve real‑world perception problems.
12 years of coding experience
10 years of employment as a software developer
A.A. Social Sciences, A.A. Social Sciences at College of San MateoBachelor of Science (BS) Economics, Bachelor of Science (BS) Economics at Rensselaer Polytechnic Institute
Spanish, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
object-detection10
computer-vision10
machine-learning10
tensorflow10
automl10
python10
efficientnet10
loss9
Programming languages (9)
Github contributions (5)
google/automl
Mar 2020 - Jun 2020
Google Brain AutoML
Role in this project:
ML Engineer
ML EngineerContributions:23 commits, 9 PRs, 52 comments in 2 months
Contributions summary:Eli primarily focused on enhancing the EfficientDet model within the Google Brain AutoML repository. Their contributions included fixing typos, making it easier to start training from EfficientDet checkpoints, and merging in master. The user also added features like DIOU NMS and label smoothing, indicating an involvement in improving model performance. Furthermore, the user made updates to the activation functions and included new layers to improve the architecture.
brainefficientnetv2pythonefficientnetdeep-learning
tfrce/taskforce
Sep 2013 - Oct 2013
Contributions:23 commits in 1 month
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