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ML/DSP Research Engineer at Bose
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Northeastern University
Doctor of Philosophy - PhD, Electrical, Electronics and Communications Engineering
January 1, 2019 – August 1, 2022
Sharif University of Technology
Master’s Degree, Biomedical Engineering /Bioelectric
January 1, 2013 – January 1, 2015
Shahed University
Bachelor’s Degree, Biomedical Engineering / Bioelectric
January 1, 2009 – January 1, 2013
Bose Corporation
Machine Learning and DSP Research Engineer
March 1, 2025 – Present
Massachusetts, United States · Hybrid
Metis Design Corporation
Signal Processing Engineer
January 1, 2024 – December 1, 2024
Boston, Massachusetts, United States · On-site
Massachusetts Institute of Technology
Postdoctoral Associate
September 1, 2022 – January 1, 2024
Cambridge, Massachusetts, United States · On-site
Bose Corporation
Machine Learning Engineer Co-op
July 1, 2020 – December 1, 2020
Greater Boston
Northeastern University
Researcher
January 1, 2019 – August 1, 2022
Boston, Massachusetts
Sharif University of Technology
Teaching Assistant for Pattern Recognition course (lecturer on deep learning using keras)
November 1, 2017 – January 1, 2018
Tehran Province, Iran
Apasai
Deep Learning researcher and developer
April 1, 2017 – December 1, 2018
Tehran Province, Iran
NTNAEEM
Computer Vision Engineer
January 1, 2016 – March 1, 2017
Tehran Province, Iran
Sharif University of Technology
Graduate Teaching Assistant for Medical Images Analysis and Processing
February 1, 2015 – June 1, 2015
Sharif University of Technology
Graduate Teaching Assistant for Pattern Recognition
September 1, 2014 – January 1, 2015
Sharif University of Technology
Research Assistant
September 1, 2013 – July 1, 2015
Object detection using deep learning for objects like sport equipment, cars, logos, and persons in the image
August 1, 2017 – December 1, 2017
Keywords: Tensorflow, Region Proposal Networks, Faster R-CNN, SSD, YOLO
Adult contents recognition using Deep Learning
April 1, 2017 – July 1, 2017
To find the probability that an image contains the adult contents. Keywords: Python, Tensorflow, CNNs, Inception-Resnet
Face Recognition using Convolutional Neural Networks
June 1, 2016 – March 1, 2017
using triplet loss instead of traditional Softmax loss inspired by Google's FaceNet paper to have 128-D representation of identity-related facial features, tried different models like VGG16, Inception-v3, InceptionResnet-v1(best results)
Identity Verification Using facial image
April 1, 2016 – April 1, 2016
Face Verification using dense SIFT features and fisher vectors.
Medical Images Segmentation using modified Fuzzy c-means clustering
January 1, 2015 – July 1, 2015
More robust segmentation method in presence of noise and bias field
Pain Level Estimation Using Facial Expression
January 1, 2013 – July 1, 2015
- Applied face detection, facial landmark extraction using Active Appearance Model (AAM), facial image registration as the preprocessing. Applied Locality Preserving Projection to reduce the dimension of data, and used linear regression to predict the intensity of pain. - Applied time-series analysis in video using Kalman filter and Particle filter to predict the pain intensity in each frame
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from medical imaging to marine mammal detection and media content analysis, demonstrates adaptability and a broad interest in applying ML across different domains. The academic background combined with industry roles at companies like Bose and MIT indicates a blend of research rigor and practical application. The experience in teaching and mentoring also suggests a collaborative and knowledge-sharing mindset. The target role of ML Engineer aligns well with the candidate's extensive experience in ML, Deep Learning, and Computer Vision.
Soft Skills & Operational Fit
The candidate's experience as a Teaching Assistant and leading machine learning interns suggests good communication and mentorship skills. Collaboration with other engineering teams (database, front-end) indicates an ability to work in a cross-functional environment. The research-heavy background implies strong problem-solving and analytical capabilities.