We are building a next-gen decision platform focused on quick iteration of rules and models.

I joined Hubla as CTO with the goal of building a world class engineering team. Here's my accomplishments:1. Increased Talent Density • Created and ran a hiring process that attracts and filters high quality Engineers. 10 folks were hired with >90% hit rate • Created a career ladder to clarify performance expectations and direct folk's growth (http://bit.ly/hubla-eng-ladder) • Adopted performance reviews, which cleared the team of low performers (about 40% non-regretted attrition)2. Created an Engineering Culture of Excellence • Increased code quality by adopting style guides and code review best practices • Increased solution quality by adopting design docs (AKA RFCs) and having all design docs go through review before implementation • Adopted a strong written culture, from code comments, to design docs, playbooks, and manuals. • Adopted a blame-less post mortem culture and and started an oncall rotation leading to a significant reliability increase3. Increased Team Efficiency and Productivity • Decreased cycle time from several weeks to ~1 day by having teams safely ship small features directly into production • Instigated and reviewed designs for simplifying Hubla's tech architecture leading to improved productivity. • Moved away from the Spotify squad model into a centralized variation of Scrum that balances end-user value and tech sustainability • Managed the tech roadmap (tech-debt and improvements)4. Acted as Product Managent Lead, performing product discoveries, leading a PM and coaching the eng team to become product focused as opposed to only tech focused.I'm very proud of how I improved Hubla's engineering capability, specially considering I took over a large team with immature engineering practices.

Before leaving Google I was leading a team of 11 engineers building the user surveys platform for Google Search, serving hundreds of client teams, thousands of engineers and collecting millions of pieces of user feedback per day. The data collected by this tool became key in improving Google Search, Assistant, News and other products. It was used to evaluate search quality, to collect data for ML model training, understand user needs, among other uses. I built highly leveraged infrastructure usable by thousands of engineers and even less technical folks. I did this by building a high performance engineering culture that was focused on quality and user value.

I grew the user surveys infra team I had built within Google Search from a few key client teams to hundreds of them across Google Search and beyond, including many more modalities of surveys, easier to user survey authoring and much more powerful analysis.Meanwhile my team grew from ~5 engineers to a multi-disciplinary team of around 13 folks, including a PM and PgM. For this work I was promoted to Sr Staff Eng.

I worked in Google Search, first revamping how user queries are sampled for Human Eval experiments. I led a team of three in the development and launch of this large system that was a great improvement in terms of speed, features and stability over its predecessor. Then I created a new system from scratch to collect collect user opinions via surveys in Google Search. I started writing a prototype, then built a team of 5 to productionalize it, leading to its adoption by several important teams within Google Search. During this process I became a manager of the team and a little later I was promoted to Staff SWE.

I worked on Google's Local Search, revamping how it's evaluated (i.e. human eval) including how local queries are sampled. This lead to a huge boost in search quality by allowing hyper-local search to be successfully evaluated.During this work I became the TL of the project including directly leading another engineer, leading to my promotion to Senior Engineer.

I worked on two projects within Google Maps:1. Launching driving directions for Colombia, which included writing a data converter from the mapping format of a data provider to Google's internal format, staging the data in Maps and then running all sorts of quality experiments to make sure the resulting user experience would be good. (A/B tests and human evaluation).2. I wrote a system to mine search logs for previously unknown informal neighborhood names. This required learning an internal logs processing language and running huge analysis jobs.Based on my independence and quality of work on these two projects I was promoted to SWE III (L4).

I worked on the Productivity Search Group within the SharePoint Organization. My team shipped the search component of SharePoint as well as Microsoft Search Server.

Worked on software for industry, including low level device drivers and SCADA systems.