Mahammad Shirinov is a software engineer with a decade of experience building scalable backends and data-driven solutions, currently contributing at Nooks in San Francisco. He combines formal training in computer engineering and mathematics with an MS from Stanford to tackle problems across software, statistics, and automation—ranging from telemedicine platform architecture to billing workflows for ad systems. As a co-founder of a telehealth startup he designed and shipped a Django/Postgres/RabbitMQ stack on AWS and integrated payments, chat, SMS, and video, showing both product and operational ownership. His research work includes a published cybersecurity algorithm for recovering query distributions from noisy data, reflecting strong applied math and algorithmic instincts. Comfortable teaching complex topics like causal inference and networks, he brings an interdisciplinary lens and a habit of iterating on product design, monetization, and go-to-market strategy.
10 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Computer Engineering and Mathematics, 3.87/4.00, Bachelor's degree, Computer Engineering and Mathematics, 3.87/4.00 at Bilkent University
Exchange Semester, Computer Science, Exchange Semester, Computer Science at EPFL (École polytechnique fédérale de Lausanne)
Master of Science - MS, Management Science and Engineering, 3.9, Master of Science - MS, Management Science and Engineering, 3.9 at Stanford University
Rendt is a platform aimed at facilitating access to computational power for running code for small-to-medium size projects by enabling execution on other people's machines in exchange for an hourly fee. It creates a good opportunity for people with powerful but underused personal machines to turn them into a source of income, and lets people lacking the hardware to perform heavy jobs (e.g. Neural Nets training, image analysis) and get their results in a timely and effortless fashion.
Contributions:88 commits, 77 pushes, 6 branches in 3 months
timelyexchangehourlyneural-netstraining
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