Machine Learning Engineer

Managing Director, Talent Acquisition | Senior Talent Partner Role: Machine Learning Engineer

Job Type:

Contract

Term:

6 Months

Workplace Type:

Hybrid / Onsite

Pay Rate:

Negotiable

Location:

Toronto, ON

Clearance:

N/A

ATS ID #:

9917

Requirements: What you'll need

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.

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.

PREFERRED SKILLS Machine Learning ML + NLP + TensorFlow or PyTorch + Python + BERT

HOW TO APPLY Patrick Marsan

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