Jonathan Sadighian
Staff Machine Learning Engineer at EvolutionIQ
Norfolk, Virginia, United States
Summary
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Top SchoolJonathan Sadighian is a Staff Machine Learning Engineer with eight years of experience building high-performance ML systems and production infrastructure across finance, insurance, and AI labs. He blends deep research chops—authoring work with 60+ citations that helped secure $10M in capital—with hands-on engineering, having implemented vectorized Cython solutions, production RL trading platforms, and agentic LLM workflows. Jonathan has repeatedly sped up pipelines and models (99% runtime reductions, 10x training speedups) while improving model performance and operational reliability. He leads distributed teams and cross-functional initiatives, coordinating up to 10 engineers across time zones, and is an active open-source contributor (900+ stars) who builds RL toolkits for real-world market data. Based in Norfolk, VA, he pairs a top-of-class MS in Computer Science with a trader’s understanding of market microstructure, enabling pragmatic, research-driven solutions that move from experiment to capital deployment.
7 years of coding experience
14 years of employment as a software developer
Bachelor's degree Business Administration and Management General, Bachelor's degree Business Administration and Management General at Pepperdine UniversityMaster's degree Computer Science, Master's degree Computer Science at École pour l'Informatique et les Techniques Avancées
English, French
Github Skills (13)
algorithm10
data-structures10
algorithms10
backend10
python10
data-structure10
data-science10
data-analysis9
cryptocurrencies9
machine-learning9
dqn9
deep-reinforcement-learning9
trading-algorithms8
Programming languages (1)
Github contributions (5)
sadighian/crypto-rl
Jun 2018 - Nov 2021
Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:3 reviews, 216 commits, 25 PRs in 3 years 5 months
Contributions summary:Jonathan contributed to the Deep Reinforcement Learning toolkit by implementing abstract base classes and methods for handling order book data. They focused on data structures, including `SortedDict`, to manage market data, and incorporated indicators such as RSI and TnS to be added to the data. The user appears to be refining the project's structure and building toward an experimental environment, while focusing on a combination of backend and data-related aspects of the project.
multithreadingcoinbasedeep-reinforcement-learningbitfinexreinforcement
nadiasjostedt/WebScrapingCraigslist
Jan 2019 - Feb 2023
Web scraping project in Python using Pandas
Contributions:1 review, 5 PRs, 11 pushes in 4 years 2 months
pythonweb-scrapingscrapingpython3scraper
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