Will Taylor
@willtaylor
Full-stack engineer with 8+ years of experience at Google and Amazon.
What I'm looking for
As a full-stack engineer with over 8 years of experience at industry giants like Google and Amazon, I have honed my skills in building customer-facing AI/ML platforms and large-scale recommendation systems. My journey has been marked by a passion for performance and usability, driving me to create features that impact millions of users. I thrive in environments where I can leverage my expertise in cloud-native applications, utilizing technologies such as Go, Python, and React.
At Google, I spearheaded the development of the Gemini platform tools, enabling over 1,000 engineers to deploy AI features through scalable applications. My work on orchestrating ML developer services on Google Cloud has been adopted by more than 10,000 developers each month. I take pride in my ability to streamline processes and enhance user experiences, as demonstrated by my contributions to various tools and dashboards that significantly improved operational efficiency.
My experience at Amazon further solidified my capabilities in developing robust recommendation systems, where I redesigned the recommendation engine to improve click-through rates by 12%. I am committed to continuous learning and innovation, always seeking opportunities to push the boundaries of technology and deliver impactful solutions.
Experience
Work history, roles, and key accomplishments
Software Engineer
May 2020 - May 2025 (5 years)
Spearheaded the development of Gemini platform tools, powering Search, Bard, and Workspace LLM integrations; enabled 1,000+ engineers to deploy AI features through scalable applications. Orchestrated ML developer services on Google Cloud, adopted by 10K+ developers/month, leveraging Terraform, Go, Angular, Cloud Build, Kubernetes, OpenAPI, and IAM policies in a multi-tenant architecture.
Software Development Engineer
Amazon
Nov 2016 - May 2020 (3 years 6 months)
Redesigned Amazon's recommendation engine from heuristic logic to deep learning models, improving CTR by 12% using SageMaker, embedding models, PostgreSQL, REST APIs, and model pipelines. Contributed to the Personalized Recommendation System for 300M+ users using distributed systems with Java, Go, Python, React, DynamoDB, SageMaker, and EC2-based deployments.
Education
Degrees, certifications, and relevant coursework
University of California, Berkeley
Bachelor of Science, Computer Science
2012 - 2016
Studied a comprehensive curriculum in computer science, gaining foundational knowledge and practical skills. Completed the program between April 2012 and November 2016.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Job categories
Skills
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