Mike Arpaia

Machine Learning

I've been building large-scale, high-performance data and machine learning systems for 15 years, with a focus on deep learning research and engineering for the last 8 years. At Moonfire, I architect and build the software, data infrastructure, and machine learning systems we use to identify and evaluate investment opportunities. Previously, at Workday, I worked on the architecture, implementation, and productionalization of distributed embedding-based search and recommendation systems.

Before that, I co-founded Kolide and worked as the lead architect and developer for our backend, infrastructure, and operating system software. At Facebook, I created and open-sourced osquery and worked on realtime analytics infrastructure. My earlier work at Etsy focused on distributed data analytics and sharded MySQL, and I later contributed to Kubernetes release automation and multi-tenancy.

My research interests include genomic foundation models and computational approaches to biological design. I work with Gonzalo Vidal at DRAGGON Lab on genomic sequence modeling, and I'm developing open-source tools for learning from biological designs and generating candidate genetic circuits. Earlier, at Mila, I worked on machine learning research and engineering for a project exploring how AI can help people understand the effects of climate change.

ML Research and Data Engineering

Moonfire

Moonfire

Managing Partner & CTO · June 2020 – Present

At Moonfire, I'm responsible for defining and executing the firm's AI-focused technical strategy. My work includes architecting and engineering our software systems, sourcing and modeling complex datasets, and building machine learning solutions to improve how we identify and evaluate opportunities. I also work closely with founders on technical and machine learning architectures and scalable data infrastructure.

Workday

Workday

Machine Learning Architect · April 2020 – November 2020

Principal Machine Learning Engineer · March 2019 – April 2020

At Workday, I worked on the architecture and implementation of a distributed embedding-based search and multi-document matching engine. We launched and scaled the system to support products across Recruiting, Learning, and Talent. As Machine Learning Architect, I also worked on the wider data science and engineering architecture, supporting research and the long-term scalability and stability of the platform.

Kolide

Kolide

Co-Founder & CTO · July 2016 – January 2019

As Co-Founder and CTO of Kolide, I worked as the lead architect and developer for almost all of our backend, infrastructure, and operating system software. I also built and led a fully remote engineering team with engineers in every US timezone.

Facebook

Facebook

Engineering Manager · October 2015 – June 2016

Staff Software Engineer (E6) · February 2014 – October 2015

At Facebook, I created and open-sourced osquery to improve operating system analytics on Linux and macOS, and worked with the team to deploy it across our corporate and production infrastructure. I later became Engineering Manager of the Intrusion Detection Infrastructure team, where we built realtime analytics systems to understand and react to activity across Facebook's infrastructure.

Etsy

Etsy

Senior Software Engineer · July 2013 – February 2014

Software Engineer · October 2012 – July 2013

At Etsy, I worked at the intersection of data science and distributed data systems, including distributed analytics infrastructure and sharded MySQL. I also built a custom host intrusion detection system to generate operating system telemetry and worked on infrastructure and application security.

Research Affiliations

DRAGGON Lab

DRAGGON Lab

October 2026 – Present

University of Bristol

Mila - Quebec Artificial Intelligence Institute

Mila - Quebec Artificial Intelligence Institute

May 2019 – May 2020

Montreal, Quebec, Canada

Government Advisory

UK Government Office for Science

UK Government Office for Science

Artificial Intelligence Advisor · July 2022 – February 2023

I helped with the Artificial Intelligence Rapid Technology Assessment throughout 2022 and 2023. During that time, I worked with the Technology and Science Insights team to map and taxonomize the modern AI and ML landscape and participated in the final presentation to over 150 people across the UK government.

Guest Lectures

ETH Zurich

ETH Zurich

Guest Lecturer

2023
University College London

University College London

Guest Lecturer

2022
London Business School

London Business School

Guest Lecturer

2023

Open-Source Infrastructure

osquery

osquery

Project Lead & Founder

2014 - 2019
Kubernetes

Kubernetes

Release Team Member

2018 - 2021

Talks & Panels

Machine Learning

How AI Systems Learn, Think & Remember

Moonfire Pulse Summit · London, UK

Tom Hulme, Mike Arpaia, Hugo Lu, Zoe von Pentz, and Raul Ferreira

2025

Governing and Controlling Complex AI Systems

Moonfire Pulse Summit · London, UK

Mehdi Ghissassi, Gerard Clos, Mike Arpaia, Max Ahrens, and Karolus Sariola

2025

Navigating the Future: AI + Tech in Venture Capital

Data-Driven VC Panel · Online

Francesco Corea, Mike Arpaia, Stephane Nasser, Philipp Omenitsch, Danish Abdullah, Ralph King, and Andre Retterath

2024

Data Infrastructure & Security

Using a Kubernetes Operator to Manage Tenancy in a B2B SaaS App

KubeCon North America · Seattle, WA

Mike Arpaia

2018

Instrumenting Dynamic Environments with Source Control, Peer Review, and Decentralized Intelligence Distribution

QueryCon · San Francisco, CA

Mike Arpaia and Zach Wasserman

2018

Starting Growing and Scaling Your Host Intrusion Detection Efforts

LocoMocoSec · Kailua-Kona, HI

Mike Arpaia

2018

Research

The Science of Venture Bets

Moonfire Research · Quantitative Finance

Mike Arpaia, Nic Lowden, and Jonas Vetterle

2025

Venture Capital Portfolio Construction and the Main Factors Impacting the Optimal Strategy

Moonfire Research · arXiv:2303.11013 · Quantitative Finance

Francesco Farina, Mike Arpaia, Harpal Khing, and Jonas Vetterle

2023

Technical Writing

Machine Learning

How to overcome poor data quality when applying ML to early-stage venture

Moonfire

Mike Arpaia

2023

An introductory reading list on AI for Scientific Discovery

Moonfire

Mike Arpaia

2023

Transformers, LLMs, and the (r)evolution of the Moonfire tech stack

Moonfire

Mike Arpaia

2023

Data Infrastructure

The Lakehouse Era

Moonfire

Mike Arpaia

2025

Using a Kubernetes Operator to Manage Tenancy in a B2B SaaS App

Kolide

Mike Arpaia

2018

Using Go for Scalable Operating System Analytics

Kolide

Mike Arpaia

2017

Research Software