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Applied Machine Learning | GenAI , AI/ML @ Apple
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Assessing your cultural and operational fit
-My areas of expertise are Machine Learning, Natural Language Processing, Computer Vision, Deep Learning and Robotics. Hands-on experience via industry exposure, projects and research, have contributed to my growth in these technical domains. -I am an articulate communicator; an energetic self-starter, skilled in quickly engaging team members and fostering strong work relationships.
Georgia Institute of Technology
Master’s Degree, Electrical and Computer Engineering
N/A – Present
University of Birmingham
Bachelor’s Degree, Electrical and Electronics Engineering
N/A – Present
Amirkabir University of Technology - Tehran Polytechnic
Electrical and Electronics Engineering
N/A – Present
Apple
Sr. Machine Learning Engineer - GenAI
June 1, 2025 – Present
Pandora
Senior Machine Learning Engineer - Search Science
April 1, 2022 – March 1, 2025
San Francisco Bay Area
Kaiser Permanente
Lead Applied ML Scientist - Search Science and Conversational AI
April 1, 2021 – April 1, 2022
San Francisco Bay Area
The Home Depot
Senior Machine Learning Scientist - Search/Natural Language Processing
October 1, 2018 – April 1, 2021
Greater Atlanta Area
Stanley Black & Decker, Inc.
Artificial Intelligence Engineer
February 1, 2018 – October 1, 2018
Atlanta Metropolitan Area
CareerBuilder
Natural Language Processing Research Internship
December 1, 2017 – February 1, 2018
Greater Atlanta Area
Emory University
Machine Learning Research Engineer
May 1, 2016 – December 1, 2017
Greater Atlanta Area
iTradeNetwork, Inc.
Internship
April 1, 2013 – August 1, 2013
Dublin, California
Amirkabir University of Technology - Tehran Polytechnic
Machine Learning/Robotics Researcher
September 1, 2011 – January 1, 2013
Robot’s path planning towards a given goal while avoiding obstacles
November 1, 2016 – Present
- Simulated the optimal path between origin and goal of a robot located on a plane in MATLAB. The robot’s movements were restricted to 3 modes: approach goal and circumvent obstacle in either clockwise or counterclockwise direction. -Accomplished individually using concept of Optimal Relaxed control.
Segmentation of Brain 3D MR Images
April 1, 2016 – August 1, 2016
• A novel algorithm called Dynamic Classifier Selection Markov Random Field (DCSMRF) for supervised segmentation MR images into three main tissues was introduced. This project led to a publication in the Journal of Springer. • DCSMRF combines a novel ensemble method called Dynamic Classifier System-Weighted Local Accuracy with MRF algorithm.
Facial Expression Recognition using CNNs and Faster Region-based CNN
February 1, 2016 – Present
Classified 32000 labeled 128×128 color images containing one of 7 emotional categories (Angary, Happy, …). Faster region-based CNN was utilized to detect the faces from the training examples. This project accomplished individually in Python by aid of TensorFlow with the accuracy rate of 75%.
Networked Control and Multi-Agent Systems
November 1, 2015 – Present
Navigated a team of 6 simulated robots through the rough terrain and located and re-activated the 8 disabled robots. This project accomplished individually. All simulations proceeded by aid of MATLAB.
Traffic Sign Detection and Recognition from Video Stream
September 1, 2015 – December 1, 2015
-Color and Shape based segmentation for the detection part and Convolutional Neural Networks for the recognition part will be used. -This project is proceeding in a team of 2. MATLAB will be utilized for the algorithm implementation.
Image classification using Convolutional Neural Network (CNN)
January 1, 2015 – May 1, 2015
Collaborated with team of 3 other graduate students to classify 60000 labeled, 256×256 color images containing one of 10 object classes (e.g. car, train …). MATLAB used for this supervised learning problem. The CNN regularized with dropout, and Softmax was replaced with a linear Support Vector Machine (SVM). Test error rate of 13.5% achieved with the resulting network (3% performance boost compared to Softmax). Participated in Image pre-processing, Data Augmentation, Optimization (Mini-Batch Gradient Descent with momentum) and Bayesian Optimization for hyperparameter tuning.
3D Remote Sensing
September 1, 2010 – July 1, 2011
- Laser scanning method utilized to render 3D Topographic Map of a 3-Acre outdoor place with accuracy of 2ft. The allocated budget for this project was $1500. - I worked on Microcontroller programming and ArcGIS software (Geographic Information System) as part of a 5-member team.
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for an innovative and technically challenging environment, particularly within the ML/AI domain. Their diverse project portfolio, ranging from robotics to medical imaging and large-scale search systems, showcases a broad interest and ability to apply ML in various contexts. The progression through senior roles at major tech companies (Home Depot, Kaiser Permanente, Pandora, Apple) indicates ambition and a drive for impact. The academic background and publication further reinforce a research-oriented and continuous learning mindset.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight collaboration (e.g., 'Collaborated on the development', 'Directed cross-functional teams') and leadership ('Designed and led the development', 'Led an initiative'). The individual projects also suggest strong problem-solving and independent work capabilities. The breadth of projects and technologies indicates adaptability and a proactive learning attitude.