Jun 2022 – Nov 2024 · 2 yrs 5 mos · San Francisco, California, United States

• Application Architecture & Performance - making Rippling ⚡ fast and highly scalable

Jun 2020 – May 2022 · 1 yr 11 mos · San Francisco, California, United States

• Enterprise Scaling Team in Core Infrastructure Area
• I lead a large effort to expand Asana's data center offering and infrastructure to APAC for Enterprise data residency requirements. I worked with almost all of our infrastructure teams to ensure that this process would be easy, toil-free, and repeatable for future infrastructure expansion efforts. Our team is now able to quickly launch new data centers without compromising stability or engineering velocity.
• I lead multiple projects and worked with our largest Enterprise customers as part of a cross-functional effort to build Asana Enterprise Key Management. I helped design and ship Comprehensive Key Verification, Encrypted Attachments, and systems to automatically handle revoked keys.
• I worked briefly on the Asana Microservices Framework, Service mesh and Envoy proxy. As Microservices Oncall lead I helped revamp our service metrics, dashboards, and define our uptime SLOs.
• Outside of Program Work
• I built the Sorting Hat - our early career team matching tool
• I proposed an Engineering Rotation program for cross-pollination and IC growth that I helped implement as an Engineering Org Initiative
• I helped champion Terraform at Asana, identified and roadmapped key Infrastructure-as-Code initiatives.

Jun 2019 – Sep 2019 · 3 mos · Seattle, Washington

• Facebook Search Team
• I worked on improving spelling correction using phonetics and unsupervised ML methods for search.

Jun 2018 – Sep 2019 · 1 yr 3 mos · La Jolla, CA

• I was an undergraduate research assistant at the Statistical and Visual Computing Lab at UCSD. I joined the Plankton Imaging project advised by Dr. Nuno Vasconcelos.
• The goal of the project was to be able to determine the family of a given several hundreds of images of a plankton specimen. The caveat is that in such biological problems, there is not a diverse wealth of specimen for each taxonomic family of plankton. We are comparing the performance of various state of the art 3D object recognition models as applied to this 'low-shot learning' biological problem.
• http://www.svcl.ucsd.edu

Jan 2018 – Sep 2018 · 8 mos · La Jolla, CA

• As a SWE intern on the Research Team at Accel Robotics, I learned to integrate various Computer Vision and Deep Learning techniques into our product.
• Initially, I incorporated object detection and face recognition models in what became our first real-time cashierless store demo. On this project, I had high impact on the functionality and design of the demo that was being showcased to investors and at conventions.
• As our project grew, my focus shifted towards in-store product detection. I built a full pipeline for training new products on various detectors and recognizers that could be used and tested in our live store demo. This included object detectors trained by Deep Neural Networks to detect and classify the wide variety of products in our store.
• My internship at Accel enabled me to greatly improve my skills in Python and UNIX through hands on experience. It allowed me to become comfortable with Git, testing, and continuous integration. I had direct impact on the product that was being developed and efficiently communicated to investors and my superiors of features that I was developing. Most importantly, I gained experience and interest in the Research and Development side of Software Engineering.

Oct 2016 – Sep 2017 · 11 mos · Bellevue, Washington

• As an intern at Gravalabs, I worked on Medzii, a drug discovery and information platform. I worked on developing a hybrid web application for Medzii using the Ionic AngularJS framework.
• The app provided a multitude of features for users from pill reminders to drug allergen information, dosages, reviews, etc. I implemented each of these features and helped integrate our user API service with the app. The result was a cohesive web app that was deployed as a website, and Android and iOS apps.
• I also worked on redesigning the underlying SQL database that held all of the pharmaceutical and user knowledge for our platform. This included cleaning up existing data that had been crawled from the web and determining a much stronger relationship model between the entities in our database such as drugs and users.
• With the database redesign, I also refactored a large portion of the user API functions on AWS lambda. These included changes that made API calls more secure and fully compatible with the features that I had implemented in the application.

Jun 2016 – Sep 2016 · 3 mos · Seattle, Washington

• Radiation Oncology Lab under Dr. Eric Ford