Machine Learning Engineer - Senior

LanceSoft Inc; Toronto; Hybrid; CAD 100,000 - 150,000; Full time

Job summary

A leading company is seeking a Senior Machine Learning Engineer in Toronto for a 12-month hybrid position. The ideal candidate will have a deep understanding of machine learning concepts, expertise in NLP, and proficiency in deep learning frameworks like TensorFlow or PyTorch. Responsibilities include implementing and tuning models, as well as data preprocessing. This role offers a dynamic environment for professionals looking to advance their skills in cutting-edge technologies.

Qualifications

• Deep understanding of machine learning concepts and algorithms.
• Expertise in NLP techniques, particularly BERT and transformer models.
• Proficiency in deep learning libraries like TensorFlow or PyTorch.

Responsibilities

• Implement, train, and fine-tune BERT models using deep learning frameworks.
• Perform text preprocessing and tokenization for NLP tasks.
• Optimize model performance through hyperparameter tuning.

Skills

Machine Learning Concepts

Natural Language Processing

Deep Learning Frameworks

Data Preprocessing

Programming in Python

Model Optimization

Transfer Learning

Job description

Title: Machine Learning Engineer - Senior
Location: Toronto, ON (Hybrid)
Duration: 12 Months with Possible extension

Experience and Skill Set Requirements

• 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. - 15%

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. - 20%

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. - 30%

Model Optimization and Tuning

• Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance. - 20%

Understanding of Transfer Learning

• Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets. - 15%

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.