João M.

Deep Learning Research Engineer

João is a Deep Learning Engineer at ASML with over 10 years of experience in artificial intelligence.

He specializes in building advanced models, including large language models (LLMs), capable of code refactoring, bug detection, and continual learning. João works extensively with PyTorch and deploys models on cloud platforms and high-performance computing systems.

Prior to ASML, he led research teams at GAIPS Lab, published in leading AI conferences, and secured competitive grants from the U.S. Air Force and FCT. He also taught AI courses, earning a Teaching Excellence Award for his contributions to education.

João’s key projects include advancing continual learning techniques, enabling AI to acquire new knowledge without forgetting previous tasks, and applying reinforcement learning to train models more efficiently with less data. He is passionate about making AI systems more effective, practical, and continually improving.

Main expertise

  • Python
    Python 10 years
  • Machine Learning
    Machine Learning 10 years
  • Data Science 10 years

Other skills

    João

    João M.

    Netherlands

    Get started

    Selected experience

    Employment

    • Deep Learning Research Engineer

      ASML - 1 year 4 months

      • Led a research team on the "LLMs for Software Engineering" project, focusing on technical debt reduction, bug detection, and documentation analysis using Large Language Models.
      • Designed, implemented, trained, tested, and deployed LLMs for automatic code refactoring and bug detection.
      • Deployed models to cloud production environments and HPC distributed computing clusters.
      • Monitored the continual performance of deployed models using tools such as MLFlow, Sacred, and Weights & Biases.
      • Connected the company’s research department with academic partners at TU/e.

      Technologies:

      • Technologies:
      • Docker Docker
      • Java Java
      • Flask Flask
      • Python Python
      • C++ C++
      • AWS S3 AWS S3
      • Azure Azure
      • Data Science
      • Google Cloud Google Cloud
      • TensorFlow TensorFlow
      • NumPy NumPy
      • OpenCV OpenCV
      • XGBoost XGBoost
      • Keras Keras
      • Caffe Caffe
      • Matlab Matlab
      • Pandas Pandas
      • Linux Linux
      • MLOps
      • Open source Open source
      • LaTeX LaTeX
      • PyTorch PyTorch
      • PyCharm PyCharm
      • Unit Testing
      • Git Git
      • Command-line interface
      • Unix Unix
      • SciPy SciPy
      • Scikit-learn Scikit-learn
      • Matplotlib Matplotlib
      • Azure ML Azure ML
      • Random Forest Random Forest
      • Clustering
      • SVM
      • PCA
      • Convolutional neural network Convolutional neural network
      • Recurrent neural network
      • Transformer Network
      • NLP
      • Machine Learning Machine Learning
      • Automation Testing
      • Boost
      • Cuda Cuda
      • Pytest Pytest
      • Apache Flink Apache Flink
      • YAML YAML
      • OpenAI API OpenAI API
      • Prompt Engineering
      • Julia Julia
      • Mojo
      • Stable Diffusion Stable Diffusion
      • Neural Network
      • Large Language Models (LLM) Large Language Models (LLM)
      • JAX
      • MLflow
      • PyTorch Lightning PyTorch Lightning
      • Slurm
      • Hugging Face Hugging Face

    • Deep Learning Research Engineer

      GAIPS Research - 5 years 8 months

      • Designed, implemented, trained, tested, and deployed state-of-the-art deep learning architectures, including Actor-Critics, DQNs, and LLMs, using convolutional, recurrent, and attention-based mechanisms for feature extraction across a wide range of tasks.
      • Deployed models to cloud production environments on platforms such as Google Cloud, Amazon AWS, and Slurm HPC distributed computing clusters.
      • Monitored the continual performance of deployed models using tools like MLFlow, Sacred, and Weights & Biases.
      • Assembled the company’s HPC Slurm cluster.
      • Led five research teams as first author, publishing a research paper for each in top-tier AI venues, including AAAI, IJCAI, ECAI, the Artificial Intelligence Journal, and PLoS One Journal.
      • Presented AI research at top-tier international conferences such as AAAI, IJCAI, and ECAI.
      • Secured two competitive funding grants, one from the U.S. Air Force Office of Scientific Research and another from the Portuguese Foundation for Science and Technology (FCT).
      • Received the Best Paper award for the project “Helping People On The Fly: Ad Hoc Teamwork for Human-Robot Teams.”

      Technologies:

      • Technologies:
      • Docker Docker
      • Python Python
      • Data Science
      • Joomla Joomla
      • NumPy NumPy
      • OpenCV OpenCV
      • XGBoost XGBoost
      • Keras Keras
      • MLOps
      • Open source Open source
      • PyTorch PyTorch
      • PyCharm PyCharm
      • Git Git
      • Command-line interface
      • SciPy SciPy
      • Scikit-learn Scikit-learn
      • Matplotlib Matplotlib
      • Azure ML Azure ML
      • Convolutional neural network Convolutional neural network
      • Recurrent neural network
      • Transformer Network
      • Machine Learning Machine Learning
      • Computer Vision
      • Boost
      • Cuda Cuda
      • Pytest Pytest
      • YAML YAML
      • OpenAI API OpenAI API
      • Neural Network
      • Hugging Face Transformers Hugging Face Transformers
      • JAX
      • MLflow
      • PyTorch Lightning PyTorch Lightning
      • Slurm
      • Hugging Face Hugging Face

    • Software Engineer

      Thales - 8 months

      • Reduced technical debt and increased overall test coverage of the Top Sky Tower solution, a tool for air traffic controllers to manage electronic strips.
      • Implemented and tested critical security detection systems.

      Technologies:

      • Technologies:
      • Java Java
      • C++ C++
      • C# C#
      • WPF WPF

    • Software Engineer

      IST IT Department - 1 year

      • Trained a Convolutional Neural Network to classify valid identity card images.
      • Implemented software for automatic and periodic backups of the university’s records to the AWS cloud.
      • Re-implemented legacy software using modern technologies such as Scala and Kotlin.

      Technologies:

      • Technologies:
      • Java Java
      • Python Python
      • AWS S3 AWS S3
      • Scala Scala
      • Kotlin Kotlin
      • TensorFlow TensorFlow
      • Keras Keras
      • PyTorch PyTorch
      • Computer Vision

    Education

    • Doctor Of PhilosophyComputer Science

      Instituto Superior Técnico · 2019 - 2025

    • MSc.Information Systems and Computer Engineering

      Instituto Superior Técnico · 2016 - 2018

    • BSc.Information Systems and Computer Engineering

      Instituto Superior Técnico · 2012 - 2016

    Portfolio

    • Multi-Task Learning & Catastrophic Forgetting in Continual Reinforcement Learning - 1
    • PyTorch Encoder-Decoder Attention Model - 1
    • odel-based Reinforcement Learning for Ad Hoc Teamwork - 1
    • TopSky Tower - 1

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