Experience

Modular

• United States
• Software Development
• 1 - 100 Employee

• AI Compiler Engineer

  • Jun 2023 - Present

  Modular is the next-generation AI developer platform unifying the development and deployment of AI for the world. The next generation of product breakthroughs will be powered by production quality infrastructure that brings together the best of compilers and runtimes, is designed for heterogeneous compute, edge to datacenter distribution, and is focused on usability. Unifying software and hardware with a "just works" approach that will save developers enormous time and increase their velocity. We are incredibly excited about the mission before us, backed by some of the best investors in the world and if you are interested in joining us - just reach out via www.modular.com/careers. Show less



OctoML

• United States
• Software Development
• 1 - 100 Employee

• Staff Engineer - Performance Engineering

  • Mar 2021 - Jun 2023

  Committer on TVM, an open source autotuning compiler for deep learning. Added features like automatic mixed precision and quantization support Lead performance efforts on key models and projects internally Create autotunable schedules for optimizing deep learning operators in GPU and CPU contexts Committer on TVM, an open source autotuning compiler for deep learning. Added features like automatic mixed precision and quantization support Lead performance efforts on key models and projects internally Create autotunable schedules for optimizing deep learning operators in GPU and CPU contexts



Apple

• United States
• Computers and Electronics Manufacturing
• 700 & Above Employee

• Machine Learning Engineer

  • Jan 2020 - Mar 2021

  A lot of cool things! A lot of cool things!



Xnor.ai

• United States
• Software Development
• 1 - 100 Employee

• Machine Learning Engineer

  • Jan 2019 - Jan 2020

  Machine Learning Team. Making machine learning truly ubiquitous. - Training face identification and detection models for low powered FPGA and ARM environments - Extending model runtime engine to support new models and functions - Created face identification demo showcasing many technologies to key executives at major tech companies Acquired by Apple. Part time Until July. Machine Learning Team. Making machine learning truly ubiquitous. - Training face identification and detection models for low powered FPGA and ARM environments - Extending model runtime engine to support new models and functions - Created face identification demo showcasing many technologies to key executives at major tech companies Acquired by Apple. Part time Until July.



University of Washington

• United States
• Higher Education
• 700 & Above Employee

• Undergraduate Teaching Assistant

  • Sep 2018 - Jun 2019

  - Autumn 2018: CSE 312 -- Foundations of Computer Science 2 (a probability course) - Spring 2019: CSE 446 -- Introduction to Machine Learning - Led 30+ students in weekly sections, graded homework, held office hours, contributed answer keys and new assignments - Autumn 2018: CSE 312 -- Foundations of Computer Science 2 (a probability course) - Spring 2019: CSE 446 -- Introduction to Machine Learning - Led 30+ students in weekly sections, graded homework, held office hours, contributed answer keys and new assignments



Sift

• United States
• Computer and Network Security
• 200 - 300 Employee

• Engineering Intern

  • Jun 2018 - Sep 2018

  - Rewrote HBase snapshot system used for offline analysis of data, deleting over 1 PB of data, saving more than 1 million dollars a year, and decreasing run times from days to hours. - Added Google BigQuery integration for HBase snapshots - Rewrote HBase snapshot system used for offline analysis of data, deleting over 1 PB of data, saving more than 1 million dollars a year, and decreasing run times from days to hours. - Added Google BigQuery integration for HBase snapshots



Facebook

• Software Development
• 700 & Above Employee

• Software Engineering Intern

  • Jun 2017 - Sep 2017

  - Added backend support for breaking ad view by frequency of demographic - Implemented statistical models to predict frequency breakdown - Created pipelines to build many training sets with different properties - Added backend support for breaking ad view by frequency of demographic - Implemented statistical models to predict frequency breakdown - Created pipelines to build many training sets with different properties



University of Washington

• United States
• Higher Education
• 700 & Above Employee

• Undergraduate Research Assistant

  • Jan 2016 - Jun 2017

  -Used scikit-learn, tensorflow to create machine learning classifiers for automatic error assessment of spirometry tests.-Worked and met quarterly with a team of doctors to discuss data collection and progress on project.-Maintained website used for data collection written in Django.

• Undergraduate Research Assistant

  • Jun 2015 - Jan 2017

  -Designed, programmed, and built a 3D printer to print sacrificial carbohydrate glass latices for use in vascular biology research.



Institute for Systems Biology

• Biotechnology Research
• 100 - 200 Employee

• Undergraduate Intern

  • Jun 2016 - Aug 2016

  -Utilized R and python to create gene expression simulation pipeline for use as ground truth in evaluating gene set analysis techniques. -Presented poster displaying work characterizing relative effectiveness of current gene set enrichment techniques. -Utilized R and python to create gene expression simulation pipeline for use as ground truth in evaluating gene set analysis techniques. -Presented poster displaying work characterizing relative effectiveness of current gene set enrichment techniques.