Jhosimar Figueroa
Mentor & ML Instructor Consultant Freelance at Superprof
Peru
Summary
👤
Senior🎓
Top SchoolJhosimar Figueroa is a machine learning researcher and engineer with 12 years of experience building end-to-end AI systems, specializing in computer vision and NLP. He combines a strong research track record—papers and presentations at NeurIPS and ICML workshops and novel contributions to deep generative modeling—with hands-on engineering, deploying models and ML pipelines using PyTorch and Keras. As a mentor and instructor he has guided 50+ students and 10+ graduate theses across top universities worldwide, maintaining top ratings on tutoring platforms while coaching candidates for SWE/ML interviews. His work bridges rigorous experimentation (e.g., competitive deep clustering and semi-supervised GMVAE results) and practical productization, informed by a solid algorithms and competitive programming background (Top 1% LeetCode in Peru). Based in Peru with an M.Sc. in Computer Science, he is now seeking Machine Learning or Research Engineer roles where he can scale impactful AI systems and continue mentoring the next generation of practitioners.
12 years of coding experience
6 years of employment as a software developer
Bachelor of Science (B.Sc.), Systems Engineering, Bachelor of Science (B.Sc.), Systems Engineering at Universidad Nacional de San AgustínMaster of Science (M.Sc.), Computer Science, Master of Science (M.Sc.), Computer Science at Universidade Estadual de Campinas
Spanish, English, Portuguese, French
Github Skills (9)
neural-network10
machine-learning10
deep-learning10
support-vector-machine10
tensorflow10
python10
image-classification10
dropout9
batch-normalization9
Programming languages (3)
Github contributions (5)
jariasf/CS231n
May 2018 - Sep 2020
My assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition
Role in this project:
ML Engineer
ML EngineerContributions:45 commits, 42 pushes, 1 branch in 2 years 4 months
Contributions summary:Jhosimar's contributions center around implementing various machine learning models, including k-Nearest Neighbor (kNN), Support Vector Machines (SVM), Softmax, and two-layer and fully-connected neural networks, demonstrating a focus on core machine learning concepts. Their work includes both naive and vectorized implementations of loss functions, stochastic gradient descent, and the integration of dropout and batch normalization techniques for regularization and improved model performance. The user appears to have successfully completed multiple assignments, indicating a strong grasp of fundamental deep learning principles and their application in image classification.
deep-learningrecognitionvisual-recognitionconvolutionalneural-networks
jariasf/GMVAE
May 2019 - Oct 2020
Contributions:10 commits, 9 pushes, 2 branches in 1 year 5 months
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