ML Scientist | Building Foundation Models
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Assessing your cultural and operational fit
Programming: C/C++, Python, Java, R, Matlab, Unix, object oriented design, parallel computing, UNIX shell scripting, data structures and algorithm development, GPU computing Machine Learning: Unsupervised/Supervised Models, Regression, Classification, scikit-learn, Probabilistic models, Markov decision networks, neural network, deep learning, CNNs, NLTK, SpaCy, Tensorflow, MLlib Database: Teradata, MySQL, MS SQL Server, OLTP, OLAP, ETL Big Data: Apache Hadoop, Yarn, Hive, Apache Spark, AWS, NoSQL databases (MongoDB), Cloudera VM, AWS EC2, AWS EMR. github: https://github.com/gurpreetgosal/Machine_Learning
University of Waterloo
Master’s Degree, Computational Mathematics
January 1, 2016 – Present
University of Ottawa / Université d'Ottawa
Master’s Degree, Electrical and Computer Engineering
January 1, 2014 – Present
Indian Institute of Technology, Delhi
Exchange Student, Microwave and RF Engineering
January 1, 2011 – January 1, 2011
Punjab Engineering College
Bachelor's in Electronics & Electrical Communication, Microcontrolers, RF n Microwave communication Systems and Digital electronics
January 1, 2008 – January 1, 2012
Cerebras Systems
Tech Lead LLMs, Senior Applied Research Scientist
July 1, 2023 – August 1, 2024
Toronto, Ontario, Canada
Cerebras Systems
Senior Member of Technical Staff
June 1, 2022 – Present
Toronto, Ontario, Canada
Cerebras Systems
Senior Applied ML Scientist
June 1, 2022 – Present
Toronto, Ontario, Canada
Huawei Technologies Canada Co., Ltd.
Senior AI Research Engineer & Tech Lead
January 1, 2019 – May 1, 2022
Toronto, Ontario, Canada
Intencion
Founder and CEO
December 1, 2018 – Present
Toronto, Ontario, Canada
AI Unsupervised meetup group
Founder
May 1, 2017 – March 1, 2020
Toronto, Canada Area
Addictive Mobility
Machine Learning Engineer
April 1, 2017 – December 1, 2018
Toronto, Canada Area
Browze
Data Scientist
November 1, 2016 – April 1, 2017
Toronto, Canada Area
University of Waterloo
Research Assistant
September 1, 2015 – November 1, 2016
Waterloo, ON
University of Ottawa
Graduate Research Assistant
September 1, 2012 – April 1, 2015
Ottawa, Canada Area
Generalized Additive Models for prediction of remote server usage in user mode.
March 1, 2016 – Present
Implemented generalized additive model (gam) in a regression problem to predict the server CPU usage in user mode given various performance parameters of the processor. Used loon (data visualization tool in R) for visualization and applied power transformation to obtain linear relationship between two explanatory variates. Classified the data set into two ranges of performance parameters given the corresponding cpu usage. Tools used: Unix, Python, Matlab, R
Robust Linear Regression Modelling for Location Determination.
February 1, 2016 – March 1, 2016
Worked on 'Finding Waldo' dataset wherein the goal was to predict (x,y) location of Waldo on a 2 page spread given a training dataset containing (x,y) locations of Waldo spotted in various pages of different books. Applied robust regression model using Tuckey's Bisquared weighting function to limit the impact of outliers on the regression model.
Applied Ensemble learning algorithm to augment classification algorithms
November 1, 2015 – Present
Implemented ensemble learning, boosting approach, to improve decision tree algorithm in a text classification in the context of spam filtering for emails.
What's Cooking
October 1, 2015 – December 1, 2015
Kaggle Competition. Worked on a natural language processing (NLP) problem. Objective was to classify a dataset of cooking ingredients where each sample is recipe from a particular cuisine (label) and based upon the label we had to classify each recipe to the cuisine it belongs to.
Inverse mapping problem in machine learning
April 1, 2013 – Present
Solved inverse mapping problems in machine learning pertinent to electrical engineering computer aided design. Proposed inverse neural network model to tackle this problem.
Neural Network Modelling for interpolation and generation of a database of Scattering Parameters required in the design of 3-d Lens Antenna
February 1, 2013 – Present
Lens Antennas Arrays are conventionally designed utilizing periodic structure analysis. Finite Element Method, MoM or Finite Difference Time Domain method simulators can perform this task to a very high degree of accuracy but the problem size is computationally very expensive. Thus, techniques like Neural Network modelling of EM structures come into play as, instead of meshing the whole problem domain and solving for fields, it actually uses already solved fields at discrete points in the domain and interpolates these curves in virtually unknown regions of design variables. Artificial Neural Network is nothing but to optimize weight parameters for each term of a summation of Gaussian function.
Introduction to Big Data
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse background spanning research, industry, and entrepreneurship. Their involvement in founding a meetup group on AGI and superintelligence suggests a strong interest in the broader implications of AI, which could align well with innovative and forward-thinking teams. However, the projects are primarily personal or academic, and the experience descriptions, while technical, do not explicitly detail collaboration or team-oriented achievements beyond 'managing the NLP team' at Huawei. This makes a full assessment of cultural fit challenging without further interview data.
Soft Skills & Operational Fit
The candidate's experience as a Tech Lead and Founder suggests leadership, initiative, and problem-solving skills. Their involvement in a meetup group indicates a proactive approach to community and knowledge sharing. The detailed project descriptions imply good communication of technical concepts.