Downtown, Toronto Hybrid: 3 Days onsite / 2 days remote 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: Robust 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.
📌 Sr Machine Learning Engineer Toronto
🏢 Source Code
📍 Toronto
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