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Global Siri, Apple
Siri and Apple are actively hiring machine learning engineers, research scientists, data scientists, and data/software engineers with a passion for ML, NLP, data science, and great software. Please get in touch if you're interested in learning more! Hello world! I am a Machine Learning Engineering Manager at Apple in Cupertino, CA. Currently, I lead an org in Global Siri that researches, builds, and ships features, models, and systems for billions of devices and users. Prior to Apple, I completed my PhD in Computer Science at MIT in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Laboratory for Financial Engineering. I was also a Fellow and Affiliate at the Harvard University Berkman-Klein Center for Internet & Society, focusing on artificial intelligence and open government datasets. My PhD focused on language technologies for understanding law, politics, and public policy. Previously, I earned master's degrees at MIT in computer science and the Technology and Policy Program. My advisors were Seth Teller, Nicholas Roy, and Jim Glass, and I focused on natural language understanding in spoken dialogue systems and assistive technology. As an undergraduate, I was an Engineering Science student (and consider myself an “engineering scientist” at heart) at Skule™ at the University of Toronto. I am also passionate about the fields of assistive technology and accessibility. I have conducted research, taught classes, and built community efforts around assistive technology, accessibility, disability, and inclusion, working collaboratively with people with disabilities to develop technologies that increase independence and broaden participation. Technology is most powerful when it empowers everyone, and I could not be prouder of being part of a company that puts accessibility at the heart of the products we make (see https://www.apple.com/ac
Massachusetts Institute of Technology
Doctor of Philosophy (Ph.D.), Computer Science
January 1, 2012 – January 1, 2016
Massachusetts Institute of Technology
MS, Electrical Engineering & Computer Science
January 1, 2009 – January 1, 2012
Massachusetts Institute of Technology
Master’s Degree, Technology and Policy
January 1, 2009 – January 1, 2012
University of Toronto
Engineering Science, Biomedical Engineering
January 1, 2004 – January 1, 2009
Apple
Senior Machine Learning Engineering Manager, Siri
October 1, 2018 – Present
Apple
Senior Machine Learning Engineer, Siri Natural Language Team
June 1, 2016 – October 1, 2018
Berkman Center for Internet & Society at Harvard University
Fellow
September 1, 2015 – January 1, 2016
Apple
Siri Speech Intern
June 1, 2013 – August 1, 2013
Apple
Data Science Research Intern, Applied Machine Learning Team
January 1, 2013 – February 1, 2013
Cupertino, Caifornia
MERL
Research Intern
June 1, 2012 – August 1, 2012
Massachusetts Institute of Technology
PhD Student Researcher / Graduate Instructor
January 1, 2009 – January 1, 2016
Cambridge, MA
Tetra Society of North America
Millennium Scholarship Grant Intern
January 1, 2009 – February 1, 2010
Alstom Power
Research and Development Engineering Intern
August 1, 2007 – July 1, 2008
Bloorview Kids Rehab
CIHR/Pfizer/RxD Student in Musculoskeletal Research
May 1, 2006 – June 1, 2007
Southern Ontario Centre for Atmospheric Aerosol Research (SOCAAR)
Faculty of Applied Science and Engineering Undergraduate
May 1, 2005 – August 1, 2006
Cultural Fit Analysis
The candidate's extensive academic background and long tenure at Apple, including management roles, suggest a fit for structured, research-driven environments. Their involvement in diverse research projects (assistive technology, environmental monitoring) indicates a broad intellectual curiosity. However, the resume is heavily focused on ML/NLP and research, which may require further assessment for a general 'Backend Engineer' role if it involves less ML-centric system design.
Soft Skills & Operational Fit
The candidate's experience as a manager and researcher suggests strong leadership, problem-solving, and collaboration skills. Their academic background and diverse project involvement indicate adaptability and a strong learning aptitude. The descriptions of managing a team and developing tools point to operational effectiveness.