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Machine Learning @ Apple | Ph.D. | AIML, LLMs, GenAI and Recommendation Systems
Driving high-impact AI/ML engineering solutions at Apple since 2017. Senior Machine Learning Engineer with a Ph.D. in Machine Learning (UFPE) and 10+ years of experience designing, building, and deploying AI/ML systems from concept to global scale. I’ve held roles as ML Engineer, Scientist, and Applied Researcher, consistently operating at the intersection of product vision and technical innovation. At Apple, I lead initiatives that bring AI/ML capabilities to billions of users, from problem framing, to ML system design, to deployment. My work spans from Privacy-Preserving App Store Ads, ML hardware tools, internal AI agents, and GenAI/LLM applications, each engineered for measurable gains in performance, reliability, and user experience. I’ve published peer-reviewed papers, filed patents, and co-created the widely adopted scikit-learn-contrib/imbalanced-learn library.
Universidade Federal de Pernambuco
Doctor of Philosophy (PhD), Machine Learning
January 1, 2014 – January 1, 2018
Universidade Federal de Pernambuco
Master of Science (MSc), Computer Science
January 1, 2012 – January 1, 2013
Universidade Federal de Pernambuco
Bachelor of Engineering (BE), Computer Engineering
January 1, 2007 – January 1, 2011
Apple
Senior Machine Learning Engineer @ Apple S&S
August 1, 2025 – Present
Apple
Senior Machine Learning Engineer @ Apple Ads
January 1, 2021 – August 1, 2025
Apple
Senior Machine Learning Engineer @ Apple Hardware
January 1, 2019 – January 1, 2021
Apple
Machine Learning Engineer @ Apple Hardware
January 1, 2017 – January 1, 2019
Anchor Loans LP
Data Scientist / ML Engineer
January 1, 2015 – January 1, 2016
Remote
VIISAR
Machine Learning Researcher
January 1, 2012 – January 1, 2016
Recife Area, Brazil
scikit-learn-contrib/imbalanced-learn
January 1, 2016 – January 1, 2017
Core contributor of imbalanced-learn, a Python package implementing several resampling techniques for heavily imbalanced data sets. It performs both majority under sampling as well as minority over sampling using cutting edge techniques including: SMOTE, Borderline SMOTE, CNN, ENN, RENN, AllKNN, Instance Hardness Threshold, Tomek Links Extraction.
brew: Python Ensemble Learning API
January 1, 2014 – January 1, 2016
BREW provides an easy API for Ensembling, Stacking, Blending, Ensemble Generation, Ensemble Pruning, Dynamic Classifier Selection, and Dynamic Ensemble Selection.
linux-diagnostics-center
January 1, 2011 – January 1, 2011
The Linux Diagnostic Center (LDC) is a software tool that enables easy access to hardware information of computers and servers. Along with the tool, the LDC also provides the libldc, a C library that allows the developer to obtain hardware information from third party projects.
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for a senior ML Engineer role, having worked extensively within a major tech company (Apple) across multiple divisions. Their involvement in open-source projects (imbalanced-learn, brew) and academic contributions (patents, peer-reviewed papers) suggest a proactive, collaborative, and innovation-driven mindset. The diversity of projects and roles within Apple (S&S, Ads, Hardware) indicates adaptability and a broad understanding of ML applications in different business contexts.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership in technical strategy, cross-functional collaboration, and managing research, indicating strong operational fit and soft skills for a senior role. The descriptions are clear and demonstrate a structured approach to problem-solving and project execution.