Wassim Seifeddine
Machine Learning Developer
Location
Paris, France
Toptal Member Since
September 23, 2022
Wassim is a software engineer with 7+ years of experience, including 4+ years in machine learning. He worked with various clients, from startups to research institutes to multinational corporations. Wassim stands out from the crowd because he is thorough about building scalable solutions that are adaptive to business requirements.
Wassim is available for hire
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Animaj
PyTorch, Data Lakes, Data Warehousing, Trend Forecasting, Topic Modeling...
Quantum Analytica
PyTorch, ETL, PySpark, REST APIs, Databases, NumPy, Jupyter Notebook, Pandas...
ACFT PERFO
Amazon Textract, Amazon Web Services (AWS), Amazon SageMaker, OCR...
Experience
Machine Learning - 4 yearsArtificial Intelligence (AI) - 4 yearsComputer Vision - 4 yearsDeep Learning - 4 yearsOptimization - 4 yearsGPT - 3 yearsGenerative Pre-trained Transformers (GPT) - 3 yearsNatural Language Processing (NLP) - 3 yearsLocation
Availability
Part-time
Preferred Environment
PyTorch, PySpark, NumPy, Jupyter Notebook, Pandas, Amazon Web Services (AWS), Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT), Computer Vision, Python, Artificial Intelligence (AI)
The most amazing...
...project I’ve built is a legal case classification model, a system that classifies case descriptions and highlights crucial text elements.
Work Experience
2022 - PRESENT
Machine Learning Engineer
Animaj
- Cultivated a concept extraction from text, audio, and video content to understand social media trends better. Concepts include emotions, activities, and sentiments on what's going on in the video.
- Developed a set of Apache Airflow Directed Acyclic Graphs (DAGs) to orchestrate data and machine learning (ML) pipelines.
- Created a monitoring system to have a bird's-eye view of the whole system.
Technologies: PyTorch, Data Lakes, Data Warehousing, Trend Forecasting, Topic Modeling, Revenue Projections, Video Analysis, Speech Synthesis, Sound, Text Animation, Diffusion Models, Image Generation, Databricks, Matplotlib, Predictive Modeling, PySpark, NumPy, Deep Neural Networks, Data Engineering, AI Design, Forecasting, Data Visualization, Algorithms, Data Analytics, OpenAI, OpenCV, Computer Vision Algorithms, Language Models, OCR, Consulting, Startup Consulting, ETL, Apache Airflow, Data Analysis, Predictive Analytics, SQL, GPT-Neo, Google Publisher Tag (GPT), Statistics, Python 3, Datasets, Causal Inference, Fine-tuning, Data Inference, Text Generation, Scikit-learn, Programming, Amazon Web Services (AWS), Speech Recognition, Architecture, Web Development, Snowflake, Analytics, AWS Lambda, NoSQL, Amazon EKS, Bash, Docker, MySQL, XGBoost, Large Language Model (LLM), AWS ELB, Amazon Elastic Container Service (Amazon ECS), AWS Fargate, API Integration, GPT-4, OpenAI GPT-3 API, Data Management, YouTube API, Youtube Ads, Optimization, Videos, Stable Diffusion, ControlNet, LoRa
2021 - PRESENT
Lead Machine Learning Engineer
Quantum Analytica
- Worked as a startup consultant for a real-estate startup to help them better understand their data infrastructure and guide them to the right tools using ETL pipelines, data lakes, delta tables, and hot storage.
- Developed a complete set of PySpark ETLs for transforming, cleaning, and normalizing data from different data sources and industries, including real estate and agriculture.
- Built a personalized ML-powered employee reward model as an MVP for an early-stage startup using customer-level data and rewards data from different providers.
- Managed the tech team, including designing the architecture for the whole team. The architecture ranges from web scrapers with dynamic proxies until the data is in hot storage, ready to be used by REST APIs.
- Developed machine learning models for consumer demand forecasters in the retail domain focusing on optimizing distribution to avoid out-of-stock.
- Worked on an algorithm for forecasting consumer demand for a certain set of products to help our client engage strategically in new markets.
Technologies: PyTorch, ETL, PySpark, REST APIs, Databases, NumPy, Jupyter Notebook, Pandas, Machine Learning, Computer Vision, Python, Artificial Intelligence (AI), Recommendation Systems, Data Science, Image Processing, Advisory, Databricks, Deep Neural Networks, Matplotlib, Data Engineering, AI Design, Forecasting, Data Visualization, APIs, Algorithms, Data Analytics, OpenAI, Predictive Modeling, OpenCV, Computer Vision Algorithms, Topic Modeling, Trend Forecasting, Data Warehousing, Data Lakes, Language Models, OCR, Data Scraping, Consulting, Technology Consulting, Slurm Workload Manager, Startup Consulting, Apache Airflow, Data Analysis, CTO, Predictive Analytics, SQL, GPT-Neo, Google Publisher Tag (GPT), Statistics, Python 3, Datasets, DeepSpeed, Causal Inference, Fine-tuning, Data Inference, Text Generation, Scikit-learn, Programming, Amazon Web Services (AWS), Quantitative Finance, Speech Recognition, Architecture, Snowflake, Analytics, NoSQL, Amazon EKS, Bash, Docker, MySQL, XGBoost, Image Recognition, Handwriting Recognition, Large Language Model (LLM), API Integration, Data Management
2023 - 2023
AI Developer
ACFT PERFO
- Implemented a solution using AWS Textract to extract structured data from PDF files.
- Developed an end-to-end pipeline for extracting and saving the data into a data lake.
- Provided alternative solutions and tools to improve the efficiency and accuracy of the data extraction process.
Technologies: Amazon Textract, Amazon Web Services (AWS), Amazon SageMaker, OCR, Artificial Intelligence (AI), Python, Machine Learning, Amazon S3 (AWS S3), Python 3, Consulting
2023 - 2023
Senior AI | Tech | HR Consultant
Block Born LLC
- Advised on AI tools to generate creative suggestions for game content production based on schema and scale.
- Reviewed and provided feedback on ideas related to implementing an AI tool for creative content suggestions.
- Demonstrated deep expertise in AI and its applications for creative content generation.
Technologies: Artificial Intelligence (AI), Consulting, ChatGPT, Plugins, GPT-4, APIs, OpenAI GPT-3 API, Stable Diffusion, ControlNet
2023 - 2023
Machine Learning Engineer
Advest Marketing, LLC
- Worked on optimizing a product ads algorithm for social media.
- Researched techniques for improving product placement in videos.
- Explored deep learning techniques for generative modeling for videos.
Technologies: Machine Learning, Python, PyTorch, TensorFlow, Deep Learning, Diffusion Models, Generative Models
2022 - 2023
ML Engineer with Skills in GPT-2/3
Toptal
- Trained a GPT-style model for simple language modeling to be deployed on a bit tensor network.
- Configured an iterative procedure to train models from previous checkpoints on new datasets.
- Monitored the model performance during and after training to know when to retrain.
Technologies: Machine Learning, Deep Learning, Python, Text Generation, Language Models, Blockchain, GPT, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP)
2021 - 2022
PhD Researcher
Nantes Université
- Worked on my Ph.D. thesis on the acceleration of a neural network using computer arithmetic.
- Devised new neural network training strategies to encourage better performances from low-precision neural networks.
- Developed custom CUDA operations for low-level operations and function approximations.
- Attended conferences and research schools related to machine learning and computer arithmetic.
Technologies: PyTorch, Arithmetic, Neural Networks, Product Acceleration, NumPy, Jupyter Notebook, Pandas, Machine Learning, Python, Artificial Intelligence (AI), C++, Data Science, Image Processing, Deep Neural Networks, Matplotlib, AI Design, Algorithms, Internet of Things (IoT), Slurm Workload Manager, Data Analysis, Statistics, Python 3, Datasets, DeepSpeed, Causal Inference, Fine-tuning, Data Inference, Scikit-learn, Programming, Bash, NVIDIA CUDA, Docker, Image Recognition, Handwriting Recognition
2021 - 2021
Research Intern
Inria
- Worked on a research project to enable running big machine learning models on small resource-constrained devices using early exit networks.
- Collaborated with two professors to do a literature review on model compression techniques, including quantization, pruning, and knowledge distillation.
- Published a paper for an international conference and presented the work at the meeting.
Technologies: PyTorch, Deep Neural Networks, IOTA, Microsoft Edge, NumPy, Jupyter Notebook, Pandas, Machine Learning, Computer Vision, Python, Artificial Intelligence (AI), C++, Data Science, Image Processing, Matplotlib, AI Design, Algorithms, OpenCV, Computer Vision Algorithms, Internet of Things (IoT), Slurm Workload Manager, Statistics, Python 3, Datasets, Causal Inference, Fine-tuning, Data Inference, Scikit-learn, Programming, Bash, Docker, API Integration
2020 - 2020
Machine Learning Engineer
Navya
- Made an object detection model faster and lighter to be able to run in pseudo real time to be deployed on a self-driving car.
- Worked closely with the core machine learning team in order to make sure we were aligned on the experiment setup and results.
- Experimented with multiple model compression strategies like pruning, quantization, and compiling to evaluate the efficacy of each method.
- Compressed the model around 4x with 3x improvement in inference speed while maintaining the same performance as the original model.
Technologies: PyTorch, Computer Vision, Object Detection, Quantization, NumPy, Jupyter Notebook, Pandas, Python, Artificial Intelligence (AI), Data Science, Image Processing, Advisory, Deep Neural Networks, Matplotlib, AI Design, Data Visualization, APIs, Algorithms, Data Analytics, Predictive Modeling, OpenCV, Computer Vision Algorithms, Consulting, Predictive Analytics, Statistics, Python 3, Datasets, Causal Inference, Fine-tuning, Scikit-learn, Programming, Web Development, Analytics, Bash, Docker, MySQL, XGBoost, Object Tracking, Image Recognition, Handwriting Recognition, API Integration
2020 - 2020
Data Scientist
SannSyn
- Developed a system that classifies legal cases from raw text input by an industry expert. The data given was raw scraped and OCR'd pdf from online sources.
- Performed data analysis of unstructured data to understand what could be done with the data and the needed processes to improve the quality.
- Built a web scraper to scrape financial news.
- Provided a sentiment analysis feature for financial news articles using transformer-based models.
Technologies: PyTorch, Scraping, Sentiment Analysis, Text Classification, Named-entity Recognition (NER), NumPy, Jupyter Notebook, Pandas, Machine Learning, Computer Vision, Python, Artificial Intelligence (AI), Data Science, Image Processing, PySpark, Deep Neural Networks, Matplotlib, Data Visualization, APIs, Algorithms, Data Analytics, Predictive Modeling, OpenCV, Computer Vision Algorithms, Data Scraping, Predictive Analytics, SQL, Statistics, Python 3, Datasets, Fine-tuning, Data Inference, Scikit-learn, Programming, Amazon Web Services (AWS), NoSQL, Bash, Docker, MySQL, XGBoost, API Integration
2019 - 2019
Machine Learning Engineer
Tedmob
- Developed a customer-facing chatbot using RASA AI, Dialogflow, and Microsoft bot framework for a leading telecom operator for handling FAQ and account-related questions with OTP authentication and third-party integrations.
- Tracked issues in real-time using various tools like Sentry, ELK stack, and Docker monitoring tools.
- Handled meeting with the client and gathered various team requirements for the best launch process.
Technologies: PyTorch, Machine Learning, Chatbots, Recommendation Systems, TensorFlow, PySpark, NumPy, Jupyter Notebook, Pandas, Computer Vision, Python, Artificial Intelligence (AI), Data Science, Image Processing, Advisory, Deep Neural Networks, Matplotlib, Algorithms, OpenCV, Computer Vision Algorithms, Consulting, Technology Consulting, SQL, Statistics, Python 3, Datasets, Fine-tuning, Scikit-learn, Programming, Amazon Web Services (AWS), Speech Recognition, Web Development, NoSQL, Bash, Docker, API Integration
2017 - 2019
Head of the iOS Department
Tedmob
- Led and oversaw a team of iOS developers for two years.
- Managed the hiring and onboarding process for new hires.
- Migrated the team tech stack and incorporated a new software architecture.
Technologies: Swift, Jira, Xcode, Jupyter Notebook, Python, Algorithms, Technology Consulting, Programming, NoSQL, API Integration, App Development
2016 - 2017
iOS Developer
Tedmob
- Developed commercial applications for clients ranging from one-person startups to multinational companies.
- Tracked issues happening on the application side in real time and resolved them in the next release.
- Migrated old applications from Objective-C to Swift.
Technologies: Swift, iOS, MacOS, Xcode, Fastlane, Algorithms, Mobile Development, Mobile App Development, Programming, NoSQL, API Integration, App Development
Experience
Concept Extraction from Videos
A complete set of ML models to extract concepts from video data, including transcripts, audio tracks, thumbnails, and the actual video footage.
The concepts include:
• Emotions
• Activities
• Sentiment
• Objects
• Movement
The concepts include:
• Emotions
• Activities
• Sentiment
• Objects
• Movement
Quantized Neural Network for Object Detection
This work was done as part of a project with a self-driving car company. The aim was to take a pre-trained model and try to run it as fast as possible and as light as possible to be used for real-time object detection.
The model used is a MobileNetV2 pre-trained model on ImageNet with an SSDLite object detector. We trained the model in an FP32 data format.
I applied several model compression techniques to reduce the model's size and monitor its performance.
Some of the methods we used are:
• Quantization
• Pruning
• Fused convolution
• Knowledge distillation
The work concluded that the model could reliably detect objects in images with the same accuracy as the FP32 version while going as low as the INT8 data format.
The model used is a MobileNetV2 pre-trained model on ImageNet with an SSDLite object detector. We trained the model in an FP32 data format.
I applied several model compression techniques to reduce the model's size and monitor its performance.
Some of the methods we used are:
• Quantization
• Pruning
• Fused convolution
• Knowledge distillation
The work concluded that the model could reliably detect objects in images with the same accuracy as the FP32 version while going as low as the INT8 data format.
Casual Language Model Fine-tuning
Iteratively fine-tuned a GPT-like simple language model on a large dataset to be deployed on a blockchain network. The training procedure included using DeepSpeed for model parallelism and custom loggers for monitoring using the weights and biases service.
Legal Case Classification
This project was threefolds:
• EDA on unstructured messy text data to understand what can be done with the data
• Build a model that would classify a legal case based on the description entered into several categories. This part aimed to provide a tool to help lawyers classify cases faster and more easily.
• Extract the entities from the case description relevant to the classification to help counterarguments.
• EDA on unstructured messy text data to understand what can be done with the data
• Build a model that would classify a legal case based on the description entered into several categories. This part aimed to provide a tool to help lawyers classify cases faster and more easily.
• Extract the entities from the case description relevant to the classification to help counterarguments.
Custom Language Model Training Framework
https://github.com/pegesund/nor_bertA dataset agnostic training framework for transformer-based language models. This project was to help bridge the performance gap between English and non-English language models. The final library should be integrated into the famous SentenceTransformer NLP package to be used directly.
Multi-modal Text Classifier
A machine learning model for extracting multiple labels from input streamed text data. I oversaw building this model end to end. The objective is to use the output of this model as a feature for another classifier.
Twitter Sentiment Analysis
https://twitter-sentiment.portfolio.wassimseifeddine.com/A text classification model that classifies the sentiment of a single tweet or a hashtag on Twitter, where the model is a transformer-based BERT model fine-tuned on a sentiment analysis dataset. The model is hosted using Streamlit.
Running Neural Networks on Edge Devices
https://ieeexplore.ieee.org/abstract/document/9664700I developed a novel technique for successfully executing parts of a single model through multiple devices like IoT, edge, and cloud while respecting each device's resource limitations. For that, I introduced a new offloading mechanism where, during computation, a decision can be made to offload work, together with the ability to exit early in the computation with intermediate results. The decision itself is tuned through deep Q-learning.
Norwegian Sentiment Analysis Model
A Transformer based neural network for classifying the sentiment of financial news into three main categories:
• Positive
• Neutral
• Negative.
The main difficulty was finding a good Norwegian labeled sentiment analysis dataset and fine-tuning an existing multilingual model.
• Positive
• Neutral
• Negative.
The main difficulty was finding a good Norwegian labeled sentiment analysis dataset and fine-tuning an existing multilingual model.
Skills
Languages
Python, Python 3, Bash, SQL, Snowflake, C++, Swift
Libraries/APIs
PyTorch, NumPy, Pandas, Matplotlib, XGBoost, PySpark, OpenCV, REST APIs, Scikit-learn, TensorFlow, YouTube API
Paradigms
Data Science, Mobile Development, ETL, App Development, Real-time Systems
Platforms
Jupyter Notebook, Docker, Databricks, Amazon Web Services (AWS), Embedded Linux, iOS, AWS Lambda, NVIDIA CUDA, Blockchain, Microsoft Edge
Storage
NoSQL, MySQL, Data Lakes, Amazon S3 (AWS S3)
Other
Machine Learning, Computer Vision, Sentiment Analysis, Deep Learning, Natural Language Processing (NLP), Artificial Intelligence (AI), Image Processing, OCR, Computer Vision Algorithms, Mobile App Development, Predictive Modeling, Algorithms, APIs, Data Analytics, Data Visualization, Forecasting, AI Design, Data Engineering, Deep Neural Networks, Data Analysis, CTO, Predictive Analytics, Statistics, Datasets, Causal Inference, Fine-tuning, Data Inference, Programming, Architecture, Analytics, Image Recognition, API Integration, GPT, Generative Pre-trained Transformers (GPT), Optimization, Consulting, Advisory, Technology Consulting, Startup Consulting, Facial Recognition, OpenAI, GPT-Neo, Google Publisher Tag (GPT), DeepSpeed, Text Generation, Speech Recognition, Web Development, Handwriting Recognition, Large Language Model (LLM), Data Management, Stable Diffusion, ControlNet, Graph Theory, Arithmetic, Object Detection, Neural Networks, Slurm Workload Manager, Chatbots, Recommendation Systems, Quantization, Data Scraping, Clustering, Text Classification, Internet of Things (IoT), Language Models, Data Warehousing, Trend Forecasting, Topic Modeling, Revenue Projections, Video Analysis, Speech Synthesis, Sound, Text Animation, Diffusion Models, Image Generation, Quantitative Finance, Object Tracking, Generative Models, ChatGPT, Plugins, GPT-4, OpenAI GPT-3 API, Amazon Textract, Youtube Ads, Videos, LoRa
Tools
Amazon EKS, Xcode, Jira, Jupyter, Apache Airflow, AWS ELB, Amazon Elastic Container Service (Amazon ECS), AWS Fargate, Amazon SageMaker
Frameworks
Flask
Education
2021 - 2022
PhD Degree in Computer Science
Nantes Université - Nantes, France
2019 - 2021
Master's Degree in Artificial Intelligence
ESIEE Paris - Paris, France
2014 - 2018
Bachelor's Degree in Computer Science
Lebanese University - Beirut, Lebanon
Certifications
MARCH 2020 - PRESENT
Deep Learning Specialization
DeepLearning.ai | via Coursera
MAY 2019 - PRESENT
Advanced Machine Learning Specialization
DeepLearning.AI and Stanford Online | via Coursera
MAY 2018 - PRESENT
Machine Learning
Stanford University | via Coursera
JULY 2016 - PRESENT
iOS Developer Nanodegree
Udacity