Hey there! I'm a software engineer with experience developing robust, efficient backend applications in Python and NodeJS. I'm also fascinated with machine learning engineering, and a proficient user of the JAX/Numpy library. Always on the lookout for fun, real-world problems!

2023 — NowEngineers GateSoftware Engineer

2023 — Now

New York, New York, United States

2021 — 20236 River SystemsSoftware Engineer

2021 — 2023

New York, United States

Core member of the performance engineering team in Shopify's warehouse robotics division. I designed and built features that enabled customers to optimize throughput in their warehouses, and improved the latency, memory usage, and reliability of the system that decided which customer orders to allocate to warehouse robots for fulfillment.

I was lucky enough to engaged in interesting work the whole time, and I learned that when performance counts, you must look beyond the abstractions to figure out what’s happening underneath, because oftentimes your assumptions will be wrong. Some of my favorite projects include:

Increased warehouse throughput and improved operator experiences by designing and implementing an event-based control system for automatically allocating work under certain conditions

Improved on-call response times by building a React app for reviewing order statuses and visualizing metrics, which engineers and operators could use to understand the behavior of the system

Improved mean decision time by 2–3x for large warehouses by optimizing DB I/O, parallelizing network requests, and refactoring the code to eliminate redundant computation and minimize strain on the garbage collector

Implemented a decision-tree based tool with Scikit-Learn and Colab to identify bugs causing suboptimal job selection

Identified and fixed a suboptimal iteration method in the popular npm library “splaytree”

2019 — 2021Eigen TechnologiesSoftware Engineer

2019 — 2021

Greater New York City Area

I extended, enhanced and built client integrations for an ML-based system for extracting information from legal docs at scale, enabling global-scale banks and law firms to better understand their documents.

At Eigen, I worked with client stakeholders and internal project managers to design and implement efficient, robust document processing pipelines that seamlessly integrated with client infrastructure. I also built generalized tools to solve commonly encountered problems when building data pipelines.

Some of my favorite work involved improving existing pipeline implementations. At one of our most notable clients, I increased the document processing rate by over 50% by parallelizing the primary extraction phase, and reduced database disk usage by over 85% with an storage solution.

2018 — 2019EssenceData Analyst

2018 — 2019

Greater New York City Area

At Essence I analyzed data from digital ad campaigns, constructed presentations and made recommendations to clients for optimizing their ad spend.

A significant percentage of analyst time was spent performing a repetitive process involving analyzing experimental data from a vendor, and building a slide deck of the results. On my own initiative, I built a tool for automatically ingesting data, performing a statistical analysis, and generating Google Slides containing the results which could then be paired down by analysts. This tool cut analyst time on this process by over 80%. The crux of the implementation was implementing the Miettinen-Nurminen confidence interval method from scratch in JavaScript, based on the original paper, for which no previous JS implementations existed.