Daniel Carlander is an Applied Scientist specializing in conversational AI and large language models, currently advancing Alexa Gen AI in San Francisco after eight years building ML systems across industry and research. He combines academic foundations in telecommunications and time-series/NLP research from UPM and UC Berkeley with hands-on experience deploying production recommender systems and fine-tuning 30B–100B parameter models for music and video experiences. His background spans network optimization at Ericsson, forecasting and NLP-driven tutoring tools, and voice assistant and Alexa Skill development—demonstrating an unusual mix of telecom-scale optimization and consumer-facing voice product expertise. Comfortable bridging research and engineering, he focuses on efficient ways to teach LLMs to use hundreds of tools and APIs, translating cutting-edge models into measurable user experience gains.
8 years of coding experience
4 years of employment as a software developer
Master of Engineering - MEng Industrial Engineering and Operations Research, Master of Engineering - MEng Industrial Engineering and Operations Research at University of California, Berkeley
High School, High School at Colegio de Fomento Las Tablas Valverde
Master of Engineering - MEng Telecommunications Engineering, Master of Engineering - MEng Telecommunications Engineering at Universidad Politécnica de Madrid
Contributions:14 pushes, 4 branches in 2 years 1 month
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Daniel Carlander - Applied Scientist II Alexa Gen AI at Amazon