RQ09508 - Sr. Machine Learning Engineer

Source Code; Toronto; Hybrid; CAD 100,000 - 130,000; Full time

Job summary

A tech company in Toronto is seeking a Senior Machine Learning Engineer to join their team. The ideal candidate will have deep expertise in machine learning concepts, particularly in Natural Language Processing (NLP) with knowledge of models like BERT. Proficiency in deep learning frameworks such as TensorFlow and PyTorch is required, along with strong programming skills in Python. This position offers a hybrid work model, combining 3 days onsite with 2 days remote.

Skills

Deep Understanding of Machine Learning Concepts

Expertise in Natural Language Processing (NLP)

Experience with Deep Learning Frameworks

Data Preprocessing Skills

Programming Skills in Python

Model Optimization and Tuning

Understanding of Transfer Learning

Tools

TensorFlow

PyTorch

NumPy

Pandas

Scikit-learn

Job description

About the job RQ09508 - Sr. Machine Learning Engineer

RQ09508 - Sr. Machine Learning Engineer

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: 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.