Mo Bitar

Karlsruhe, Baden-Württemberg, Germany
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts

Summary

🤩
Rockstar
Mohamad Bitar is a Senior iOS Engineer based in Munich with five years of experience building user-centric mobile front ends using Swift, SwiftUI and UIKit for smart access and e-learning products. At SimonsVoss (Allegion) he shapes apps for building access systems and collaborates closely with UI and back-end teams to ship polished, secure experiences. Previously he led mobile and web front-end work at AlifBee and has full‑stack experience from earlier roles, giving him a strong product-oriented perspective. Beyond iOS, he contributes to open-source projects (including fixes to a Julia reinforcement learning package) and maintains Rustlings, signaling a curiosity for systems and ML tooling not obvious from his mobile-focused titles. He holds a bachelor’s in Computer Software Engineering from Damascus University and is known for attention to detail, continuous learning, and pragmatic problem solving.
code5 years of coding experience
github-logo-circle

Github Skills (9)

deep-reinforcement-learning10
machine-learning10
reinforcement-learning10
julia10
data-structure9
algorithm9
data-structures9
algorithms9
python7

Programming languages (22)

C#C++RustCSchemeChapelGoHTML

Github contributions (5)

github-logo-circle
A reinforcement learning package for Julia
Role in this project:
userML Engineer
Contributions:1 review, 5 commits, 5 PRs in 4 days
Contributions summary:Mo contributed to the reinforcement learning package by fixing bugs and improving the handling of actions within the environment. They addressed issues related to dummy actions, especially for continuous and interval action spaces, and made necessary adjustments to how these actions are selected and processed. The user also fixed a warning related to keyword arguments and removed an unneeded method from the Monte Carlo learner, which likely improved the efficiency of the code.
juliareinforcement-learningmachine-learningdeep-reinforcement-learningdeep-q-network
mo8it/rustlings

Aug 2023 - Apr 2024

Rustlings fork with collective-score integration. Only use it in my Rust course!
Contributions:29 pushes, 45 branches in 7 months
rust
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.
Request Free Trial