Summary
Jenia Golbstein is a Machine Learning Researcher with 8 years of hands-on experience building and deploying deep learning solutions for computer vision, time series and high-dimensional sensor data. She has developed and optimized CNNs, object detection, segmentation, super-resolution, Siamese networks and GANs across 2D–4D inputs and has productionized models on NVIDIA Jetson and AWS EC2 GPU instances. Her background spans R&D and product-facing roles—moving from image-algorithm pipelines and analytics dashboards to real-time embedded inference for video, point clouds and radar. Proficient in Keras, TensorFlow, PyTorch and MXNet, she also brings classical ML expertise (SVM, PCA, GLM, clustering) and Unix/docker tooling to bridge research and engineering. An active GitHub and StackOverflow contributor and creator of several popular repos, she combines academic training in biomedical engineering and computer science with a pragmatic focus on making complex models interpretable and deployable. Based in Tel Aviv, she is particularly skilled at squeezing state-of-the-art models into resource-constrained environments without sacrificing accuracy.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science (BSc), Biomedical/Medical Engineering, Bachelor of Science (BSc), Biomedical/Medical Engineering at Ben-Gurion University of the Negev
Russian, Hebrew, English