Marcio Fonseca
ML EngineerMachine Learning Engineer, Legislative Analyst
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine Learning Engineer, Legislative Analyst
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I'm a Machine Learning Engineer at the Brazilian Chamber of Deputies with a PhD in Natural Language Processing from the University of Edinburgh, where I was supervised by Prof. Shay Cohen (Institute for Language, Cognition and Computation). My current focus is on developing agentic systems that leverage both generative AI and traditional machine learning to tackle challenges in the legislative domain.
The University of Edinburgh
Doctor of Philosophy - PhD, Natural Language Processing
December 1, 2020 – May 1, 2024
The University of Edinburgh
Master's degree, Cognitive Science
January 1, 2017 – January 1, 2018
Udacity
Self-Driving Car Engineer Nanodegree, Artificial Intelligence
January 1, 2016 – January 1, 2017
Instituto Militar de Engenharia
Bachelor of Science (BS), Computer Engineering
January 1, 2002 – January 1, 2006
Colégio de Aplicação da UFRJ
High School
January 1, 1995 – January 1, 2001
Câmara dos Deputados
Machine Learning Engineer
June 1, 2024 – Present
Brasília, Federal District, Brazil · On-site
The University of Edinburgh
Doctoral Student
December 1, 2020 – May 1, 2024
Edinburgh, Scotland, United Kingdom · On-site
Câmara dos Deputados
Machine Learning Engineer
February 1, 2017 – December 1, 2020
Brasília Area, Brazil
Cupom Social
Founder
September 1, 2010 – December 1, 2011
Brasília Area, Brazil
Câmara dos Deputados
Software Engineer
February 1, 2010 – February 1, 2017
Brasília Area, Brazil
Brazilian Army (Exército Brasileiro)
Software Architect
February 1, 2007 – February 1, 2010
Brasília Area, Brazil
Advanced Lane Finding
February 1, 2017 – Present
Software pipeline to identify the lane boundaries in a video. Includes camera calibration, perspective transforms using OpenCV and polynomial fit to calculate radius of curvature and offset from lane center.
Traffic Sign Classification
December 1, 2016 – Present
Built and trained a deep neural network to classify traffic signs, using TensorFlow. Experimented with different network architectures. Performed image pre-processing and validation to guard against overfitting.
A predictive model for project risk analysis
August 1, 2016 – Present
This publication presents an application of machine learning classification models for risk analysis in IT projects using the SciPy stack. This work was accepted for the Second Seminar on Data Analysis in Public Administration at the Federal Court of Accounts (TCU - Brazil).
Cryptography I
Coursera
June 24, 2026 – Present
Probabilistic Graphical Models 1: Representation (with Honors)
Coursera
June 24, 2026 – Present
Duolingo Proficiency Exam in English: Expert
Duolingo
June 24, 2026 – Present
Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse background, including academic research, government roles, and entrepreneurial ventures, suggests adaptability and a broad perspective. Their involvement in public administration data analysis and open data initiatives indicates a potential alignment with roles requiring societal impact or public service. The long tenure at Câmara dos Deputados (albeit in different roles) shows commitment. The target role of ML Engineer aligns well with their recent experience and academic focus. However, the lack of specific company culture data makes a precise cultural fit assessment challenging.
Soft Skills & Operational Fit
The candidate's experience as a Lead Software Engineer and Software Architect, including mentoring roles, suggests strong leadership and team collaboration skills. Their entrepreneurial experience as a Founder also indicates initiative and problem-solving abilities. The PhD research demonstrates strong analytical and independent work capabilities. However, without psychometric test results, a definitive assessment of work attitude, stress handling, and team collaboration is limited.