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Backend Engineer with Machine Learning experience
I’m a software engineer with M.S. degrees in Applied Mathematics and Electrical Engineering from the University of Michigan. Towards the end of grad school, I developed an interest in Linear Algebra and its applications through the classes “Numerical Linear Algebra” and “Estimation, Detection, and Filtering”. These courses taught me how to uncover patterns in underlying data and use that to discover new insights for solving problems. Since then, I’ve become engrossed in Machine Learning and Analytics - specifically using the appropriate Machine Learning algorithms to address problems. These fields deeply interest me, and I’ve pursued them constantly through personal projects and classes. I’m a firm believer that life is a continuous learning adventure, and that collaboration and self-learning go hand-in-hand to tackle challenging problems. Skills: ► Languages: Python, Scala, SQL, Spark ► Concepts: Machine Learning, Data Mining, API design, System Design
University of Michigan
Master of Science (MS), Applied Mathematics
January 1, 2008 – January 1, 2011
University of Michigan
Master of Science (MS), Electrical Engineering:Systems
January 1, 2008 – January 1, 2011
UC San Diego
BS, Electrical Engineering
January 1, 2004 – January 1, 2008
LaunchDarkly
Senior Backend Engineer
March 1, 2025 – Present
Hybrid
Bake Sum
Baker
September 1, 2023 – February 1, 2025
On-site
Rize Up Sourdough
Dough Team
September 1, 2023 – February 1, 2025
On-site
Amplitude
Senior Machine Learning Engineer
February 1, 2021 – April 1, 2023
Optimizely
Senior Software Engineer - Machine Learning Products & Analytics
October 1, 2019 – February 1, 2021
Optimizely
Software Engineer - Machine Learning Products
June 1, 2017 – October 1, 2019
Storm8
ML Engineer / Data Engineer
February 1, 2016 – June 1, 2017
Storm8
Senior Software Engineer
June 1, 2015 – June 1, 2017
Storm8
Software Engineer
October 1, 2014 – June 1, 2015
Cisco Systems
Software Engineer II
September 1, 2011 – September 1, 2014
San Jose
Cisco
Graduate Intern
May 1, 2010 – August 1, 2010
University of Michigan
Graduate Student
January 1, 2009 – May 1, 2009
ViaSat
Intern
January 1, 2008 – August 1, 2010
Carlsbad
Triton Engineering Student Council
Disciplines of Engineering Career Fair (DECaF) Chair
June 1, 2007 – June 1, 2008
University of California, San Diego
Los Alamos National Laboratory
Software Engineer Intern
June 1, 2007 – August 1, 2007
Los Alamos, NM
HKN (Eta Kappa Nu) - ECE and CSE Honor Society
President/Co-founder
September 1, 2006 – June 1, 2007
University of California, San Diego
Kaggle SciKit Project
January 1, 2014 – Present
Contributing to GitHub repository. Exploring different methods to analyze data given in Kaggle.
Natural Language Processing (NLP) Project
December 1, 2013 – Present
I utilized Python’s rich ML libraries (numpy, scipy, sklearn) to tokenize the training set of tweets, apply sklearn's tf-idf vectorizer to remove stopwords, and train a multinomial Naive Bayes classifier. The resulting conditional probabilities were used for classification on the test set and the results were ~57% classification accuracy.
Data Mining/Extraction & Data Analysis Project
September 1, 2012 – December 1, 2012
Wrote a PERL script to clean financial transaction outputs, extracting from thousands of records of unstructured transactional data, to be organized and reconstructed into a database-accessible format. This data was then used to train a linear and piecewise linear models in MATLAB and compare their performances when evaluating the risks of companies.
Data Mining, Principal Component Analysis Project
August 1, 2012 – August 1, 2012
Created PERL application to mine data of issued loans, classify features, and then perform dimension reduction in MATLAB. Explanatory data analysis was created using various subsets of numerical, categorical, and unstructured/text data to assess minimum dimensionality. Implemented a threshold power method to identify most important components.
Algorithms: Design and Analysis
Coursera
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse professional background, including significant experience in tech companies like Amplitude, Optimizely, and Cisco, alongside recent roles in baking. While the tech experience aligns well with a data-driven culture, the recent non-technical roles might raise questions about current technical engagement and career trajectory. The project diversity, ranging from academic data mining to Kaggle contributions, shows initiative. However, the target role of 'Data Analyst' is a slight step down from 'Senior Machine Learning Engineer' or 'Senior Backend Engineer', which could indicate a shift in career focus or a desire for a different type of challenge. The long tenure in engineering roles suggests stability, but the recent career break into baking might need clarification regarding long-term career goals and commitment to a technical role.
Soft Skills & Operational Fit
The candidate's experience in leading projects (multi-armed bandit feature, DECaF Chair, HKN President) and collaborating with cross-functional teams (designers, infra engineers, frontend engineers, statisticians) indicates strong leadership, collaboration, and communication skills. Their ability to productize complex statistical methods and optimize systems suggests a results-oriented and problem-solving mindset. The diverse work history, including non-technical roles, might indicate adaptability and a broad perspective, though the recent non-technical roles are a deviation from the target role.