Led the long-term engineering vision of the language of preference feature within Amazon Search and how to ensure optimal customer experience through seamless collaboration of multiple verticals within the organization (language detection, spell correction, machine translation, query understanding). Spearheaded cross-team initiatives across organizational codebases in order to improve the synergy of the systems involved in the search process. Directly contributed to the launch of 33 languages of preference, across 12 Amazon Retail stores worldwide.Authored and executed a year-long roadmap to integrate a new methodology into the model training process, utilizing Search signals to enrich query-based datasets. Conducted a proof of concept experiment to validate feasibility and identify technical obstacles. Documented and categorized available signals, presenting findings to scientists and facilitating brainstorming sessions for experiment ideation. Recommended focusing experiments on select languages and conducting side-by-side comparisons to determine the most effective approach. As a result, successfully rebuilt 14 language models, significantly enhancing overall customer experience.Successfully managed organization-wide yearly hardware planning and allocation of a $1.3M USD budget. Facilitated driving efficiency improvement programs across the organization and orchestrated the migration of our main model training workloads to a cheaper AWS region. As a result of improved efficiency, teams in the organization were able to satisfy increased GPU hardware demands even as the instance price increased, driven by large company-wide demand in the recent 2-3 years.

Developed a highly scalable translation cache to optimize the translation experience for frequently searched terms. Collaborated with country expansion PMs and leadership to devise a self-refreshing method ensuring translation freshness, alongside an emergency override process reducing correction time from days to 15 minutes. Redesigned the cache to adapt to evolving customer scaling needs and facilitate A/B testing of cache contents. Orchestrated the migration plan and milestones for Tier-1 service transition to the new cache, executing the majority of the migration seamlessly without operational disruptions.Mentored and coached engineers, guiding them through and instilling engineering best practices and standards. Led multiple cross-organization initiatives to improve engineering excellence, hardware efficiency and best practices on software testing and deployment.

Successfully scaled the machine translation API to accommodate the language preference feature on the Amazon Retail website, leading to a 100x increase in synchronous traffic. Designed and implemented critical system components such as A/B testing, caching, client isolation, and throttling. Assessed hardware redundancy needs and associated costs. Established an automated system for creating and updating monitors and alarms for new language additions.As engineering lead, managed integration into the Amazon ecosystem for one licensed third-party code scanning solution, two software company acquisitions, and three external machine translation providers. Collaborated with leadership and legal teams to conduct due diligence, ensuring adherence to Amazon's engineering standards and policies. Facilitated the conversion to open source or internal import of multiple third-party software systems.

Led design and implementation of internal machine translation API used to interface with both internal and external machine translation providers. As lead engineer supported the growth of the API to support hundreds of clients and its evolution into a Tier-1 service, for which I developed and executed a comprehensive tech readiness plan. This API is to this day the primary method of obtaining machine translations at Amazon, processing on average 500MM translation requests weekly.

School of Computer Science and Communication, Department of Computational BiologyImplemented highly-scalable and performant C/C++ variants of neural network algorithms and models developed within the lab, optimized for execution on highly-distributed architectures (Cray supercomputers).Overhauled the floating point arithmetic implementation of a neural simulator used within the lab, leading to a 10x increase in throughput.Implemented software best practices within the lab, including deploying and maintaining a version control server (GitLab), unit and integration testing and CI/CD pipelines. Organized trainings for the lab staff to help with onboarding.

Computational neuroscience research intern at Lansner lab

Developed a comprehensive port management software solution in PHP/JavaScript (AJAX) used to track daily arrivals of vessels and invoicing. Provided training and initial operational support. The solution has been in daily use since 2009 and requires no ongoing support or maintenance.

Mentoring first-year university students, helping them adapt to the university and solve problems during the course of their studies

Assisting students with solving their laboratory assignmentsOral examination of students after their laboratory assignmentsDevelopment of new laboratory assignments

Development of interactive homework problems in Java

Assisting students with solving their laboratory assignmentsOral examination of students after their laboratory assignments

Closed beta tester for the game Mytheon.

Assisting students with solving their laboratory assignments

QA testing of the TimeTracker 3.1 application

Closed beta tester for the video game "Dance".