Elad Nachmias
Artificial Intelligence Researcher at Decart
Israel
Summary
👤
Senior🎓
Top SchoolElad Nachmias is an AI researcher and engineer with a decade of hands-on experience building production-ready ML systems and research-driven algorithms, currently at Decart after leading perception and RL research at General Motors. He combines strong academic credentials from Technion (MSc with top grades and a thesis) with practical expertise in reinforcement learning for real-time autonomous driving, distributed training, and end-to-end experimental pipelines. A lifelong developer who started coding at age 10, Elad has contributed to notable open-source projects like code2vec—adding Keras attention layers and modern tf.data refactors—demonstrating both ML research depth and backend engineering rigor. He has a track record of turning proofs-of-concept into production (e.g., product architect at FunKeyword) and a knack for bridging simulation research with deployable systems.
10 years of coding experience
3 years of employment as a software developer
Master of Science (MS.c.) with thesis, Computer Science, Courses: 97.5; Thesis: 95, Master of Science (MS.c.) with thesis, Computer Science, Courses: 97.5; Thesis: 95 at Technion - Israel Institute of TechnologyThe Hebrew Reali School
English, Hebrew, French
Github Skills (5)
attention-mechanism10
keras10
tensorflow10
python10
machine-learning9
Programming languages (8)
Github contributions (5)
tech-srl/code2vec
Mar 2019 - Sep 2019
TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:91 commits, 3 PRs, 5 comments in 6 months
Contributions summary:Elad contributed to the development of the code2vec project, which involves learning distributed representations of code using TensorFlow. Their commits show they added a Keras AttentionLayer, integrated dropout and DL_FRAMEWORK configuration parameters, and implemented and refined Keras model components. The user also worked on refactoring and improving the code, including the use of an iterator instead of creating a list in common.py and refactoring to support the use of the `tf.data` API for data loading.
information-theoryautoencoderdistributed-learningrepresentationsdistributed-representations
eladn/applyxn-c-macro
May 2018 - Nov 2020
Contributions:6 commits, 3 pushes, 1 branch in 2 years 5 months
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