Verified Expert in Engineering
Artificial Intelligence (AI) Developer
Danilo is a machine learning engineer with a master's degree in computer science from the Nagoya Institute of Technology and 7+ years of experience in automatic speech recognition (ASR), natural language processing (NLP), and audio classification. From research to production, he's been involved in all steps of the ML lifecycle. He also designs data pipelines and performs training, evaluation, and deployment of deep learning models for education and business intelligence applications.
Linux, Python, Vi
The most amazing...
...production system I've built was a speech recognition back end with custom acoustic and language models that serves 200,000+ requests per week.
Machine Learning Engineer
- Designed a training pipeline for BERT-based text-to-text models, including data loading, text preprocessing, training, validation, tracking of metrics, logs, and output files.
- Trained and evaluated text-to-text models and benchmarked them against third-party services and legacy systems used by the company.
- Wrote new back-end APIs for legacy NLP systems to migrate them to a new infrastructure managed by the Hashicorp Nomad container orchestrator.
- Created and implemented a complete data pipeline for an NLP-based semantic search system, including the periodic check for new files to be processed, text embeddings generation and storing, and the back-end API.
Machine Learning Expert
Online Learning Platform (Toptal Client)
- Trained, evaluated, and improved automatic speech recognition (ASR) models. The final model more than halved the error rate compared to Google Speech Recognition for the same task.
- Designed a scalable back-end API that receives audio files in any format and sampling rate and returns the text recognition result.
- Deployed the system to GCP Cloud Run and monitored its results. The system had 200,000+ requests per week as of early September 2021.
- Created data preprocessing tools in Python to retrieve the client's audio data from GCP buckets and prepare it for ASR model training and evaluation.
- Designed training and testing tools for audio classification in speech recognition using TensorFlow.
Yume Technology Co. Ltd. (Assigned to Honda Research Institute Japan)
- Co-authored a system that achieved the third-best and sixth-best team results for two different metrics of the DCASE Challenge 2018, task 5.
- Implemented a server cluster structure that increased the speed of speech-model training five-fold compared to training models on a single server.
- Kick-started using deep learning models for speech recognition at the company, replacing legacy Hidden Markov models with DNNs, CNNs, attention models, and others.
- Trained and evaluated acoustic and language models for speech recognition in English and Japanese, targeting several applications such as robots, cars, and meetings.
- Maintained Git repositories for software tools and frameworks to be shared with other developers while fixing bugs and adding new functionalities as requested.
- Analyzed and summarized academic papers to grasp the state-of-the-art techniques in relevant fields and evaluate which ones could be integrated into the frameworks used by the team.
- Evaluated an audio dereverberation algorithm and its effects on speech recognition performance and ported its MATLAB implementation to C++.
Junior Development Engineer
Siemens Enterprise Communications (Now Unify)
- Designed and assembled microcontroller programming tools that saved the company €2,500 per piece.
- Assessed telephone hardware problems reported by the customer support team and submitted firmware fixes.
- Validated country-specified settings for telephone hardware before its international release.
- Received a promotion to this position in mid-2010 after serving as an engineering intern.
Acoustic Event Recognition System for the DCASE Challenge 2018, Task 5
Our proposed solution was a multi-task CNN network with shared filters between the tasks. I was in charge of fixing and improving a preexisting TensorFlow implementation, training and tuning the models, and writing the technical report.
Our implementation achieved the third-best team result for the known microphone array and the sixth-best team result for the unknown microphone array.
Environmental Sound Recognition Aiding Device
Python, Bash, VHDL, C, C++, Embedded C
Git, Kaldi, Pytest, Grafana
Linux, Docker, Amazon Web Services (AWS), Google Cloud Platform (GCP)
Machine Learning, Deep Learning, Artificial Intelligence (AI), Speech Recognition, Vi, Natural Language Processing (NLP), Speech to Text, Neural Networks, ASR, Conda, GPT, Generative Pre-trained Transformers (GPT), Digital Signal Processing, PCB Design, Embedded Hardware, Software Design, Slurm Workload Manager, FPGA, APIs, Back-end, Nomad, Prometheus, DSP
NumPy, Scikit-learn, TensorFlow, Pandas
Databases, PostgreSQL, MySQL
Master's Degree in Scientific and Engineering Simulation
Nagoya Institute of Technology - Nagoya, Japan
Bachelor's Degree in Electronics and Telecommunications
Federal University of Technology - Curitiba, Parana State, Brazil