Experience

Atomic AI

• United States
• Biotechnology Research
• 1 - 100 Employee

• Founding Engineer & Engineering Lead

  • Nov 2021 - Present

  Keeping the company connected by architecting compute+data solutions and the engineering team to build them. Using Kubernetes, Bazel, JAX, S3, RDS, and Python. - Managing several engineers - Multi-cloud Kubernetes (EKS/GKE/self-managed) running both CPU and GPU accelerated workloads - Cost effective ML inference solutions - Flexible build environment to enable taking an experimental program and running it at scale on cluster hardware with minimal overhead - Containerless… Show more Keeping the company connected by architecting compute+data solutions and the engineering team to build them. Using Kubernetes, Bazel, JAX, S3, RDS, and Python. - Managing several engineers - Multi-cloud Kubernetes (EKS/GKE/self-managed) running both CPU and GPU accelerated workloads - Cost effective ML inference solutions - Flexible build environment to enable taking an experimental program and running it at scale on cluster hardware with minimal overhead - Containerless pipeline solutions to enable rapid iteration on analysis jobs - On-prem/colo networking and GPU compute planning, acquisition, installation, and management - Pulumi and Terraform for infrastructure as code - NixOS, Ansible, Saltstack, and Tailscale Show less Keeping the company connected by architecting compute+data solutions and the engineering team to build them. Using Kubernetes, Bazel, JAX, S3, RDS, and Python. - Managing several engineers - Multi-cloud Kubernetes (EKS/GKE/self-managed) running both CPU and GPU accelerated workloads - Cost effective ML inference solutions - Flexible build environment to enable taking an experimental program and running it at scale on cluster hardware with minimal overhead - Containerless… Show more Keeping the company connected by architecting compute+data solutions and the engineering team to build them. Using Kubernetes, Bazel, JAX, S3, RDS, and Python. - Managing several engineers - Multi-cloud Kubernetes (EKS/GKE/self-managed) running both CPU and GPU accelerated workloads - Cost effective ML inference solutions - Flexible build environment to enable taking an experimental program and running it at scale on cluster hardware with minimal overhead - Containerless pipeline solutions to enable rapid iteration on analysis jobs - On-prem/colo networking and GPU compute planning, acquisition, installation, and management - Pulumi and Terraform for infrastructure as code - NixOS, Ansible, Saltstack, and Tailscale Show less



Google

• United States
• Software Development
• 700 & Above Employee

• Senior Software Engineer

  • May 2018 - Oct 2021

  TL for Atlas and two other projects, enabling visualization, curation, and evaluation of Google's map data using WebGL.

• Software Engineer

  • Aug 2013 - May 2018

  TL for Atlas, enabling visualization and curation of Google's map data. Previously: Focused on G Suite customer onboarding systems (marketing content, signup flows, domain registrations, and DNS management) in order to optimize growth.



University of California, Davis

• United States
• Higher Education
• 700 & Above Employee

• Assistant IV

  • Mar 2011 - Jun 2013

  Developing Eclipse plugins to make research software easier to use. Developing Eclipse plugins to make research software easier to use.



Google

• United States
• Software Development
• 700 & Above Employee

• Software Engineering Intern

  • Jun 2012 - Sep 2012

  Developed an internal GWT application to enable engineers to debug broken data in a proprietary production system. Developed an internal GWT application to enable engineers to debug broken data in a proprietary production system.