Julia Moseyko
Member Of Technical Staff at Artificial Analysis
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolJulia Moseyko is a Member of Technical Staff and AI strategist with nine years of experience applying machine learning and product leadership across startups, venture, and research organizations. Trained at MIT, she has a strong research pedigree in reinforcement learning and perception—contributing to publications at ICRA and ICML and porting simulators into OpenAI Gym environments for autonomous driving work. Her background blends hands-on ML engineering (including fixes and architecture work on the popular MIT intro to deep learning labs) with product and go-to-market roles at McKinsey QuantumBlack, Pear VC, and early-stage ventures. She has built real-time perception systems for autonomous delivery and prototyped algorithmic trading under NDA, showing comfort with high-stakes, production-adjacent projects. Equally at home in strategy and code, she leverages technical depth to inform product decisions and venture sourcing for deep-tech startups. Based in New York, she brings a rare combination of published research experience, startup founding instincts, and operational fluency.
9 years of coding experience
6 years of employment as a software developer
Miramonte High SchoolBachelor of Science (S.B.) Computer Science and Engineering, Bachelor of Science (S.B.) Computer Science and Engineering at Massachusetts Institute of Technology
English, French, Russian
Github Skills (8)
neural-network10
pytorch10
jupyter-notebook10
deep-learning10
python10
reinforcement-learning10
tensorflow9
computer-vision9
Programming languages (2)
Github contributions (5)
MITDeepLearning/introtodeeplearning
Jan 2019 - Jan 2019
Lab Materials for MIT 6.S191: Introduction to Deep Learning
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 11 pushes, 1 branch in 1 day
Contributions summary:Julia primarily focused on modifying and debugging code related to reinforcement learning models within the context of a deep learning tutorial. Their contributions involve fixing syntax, and addressing "TODO" comments within the provided notebooks. The code changes mainly center around defining neural network architectures, including convolutional layers and output dimensions, specifically for a reinforcement learning environment. Additionally, the user made modifications to functions related to normalizing rewards.
deep-learningmitneural-networkstensorflowtensorflow-tutorials
julia0/handwriting-recognition-neural-network
Oct 2016 - Oct 2017
Contributions:2 pushes, 1 branch in 11 months
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