Machine Learning Engineer Senior

Toronto - Canada

Monthly Salary: Not Disclosed

Posted on: 2 days ago

Vacancies: 1 Vacancy

Job Summary

Experience and Skill Set Requirements

(15%) Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts algorithms and techniques.
Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models especially BERT and other transformer-based models for tasks like text classification sentiment analysis and language understanding.

(20%) Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing training and fine-tuning BERT models using these frameworks is crucial.

(30%)Data Preprocessing Skills: Ability to perform text preprocessing tokenization and understanding of word embeddings.
Programming Skills: Strong programming skills in Python including experience with libraries like NumPy Pandas and Scikit-learn.

(20%) Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.

(15%) Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.

Must Haves:

Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts algorithms and techniques.
Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models especially BERT and other transformer-based models for tasks like text classification sentiment analysis and language understanding.
Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing training and fine-tuning BERT models using these frameworks is crucial.
Data Preprocessing Skills: Ability to perform text preprocessing tokenization and understanding of word embeddings.
Programming Skills: Strong programming skills in Python including experience with libraries like NumPy Pandas and Scikit-learn.
Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.
Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.

Experience and Skill Set Requirements (15%) Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts algorithms and techniques. Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models especially BERT and other transformer-...

Experience and Skill Set Requirements

(15%) Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts algorithms and techniques.
Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models especially BERT and other transformer-based models for tasks like text classification sentiment analysis and language understanding.

(20%) Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing training and fine-tuning BERT models using these frameworks is crucial.

(30%)Data Preprocessing Skills: Ability to perform text preprocessing tokenization and understanding of word embeddings.
Programming Skills: Strong programming skills in Python including experience with libraries like NumPy Pandas and Scikit-learn.

(20%) Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.

(15%) Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.

Must Haves:

Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts algorithms and techniques.
Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models especially BERT and other transformer-based models for tasks like text classification sentiment analysis and language understanding.
Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing training and fine-tuning BERT models using these frameworks is crucial.
Data Preprocessing Skills: Ability to perform text preprocessing tokenization and understanding of word embeddings.
Programming Skills: Strong programming skills in Python including experience with libraries like NumPy Pandas and Scikit-learn.
Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.
Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.