Job Description
Machine Learning Engineer (NLP & Deep Learning Focus)
Location: Hybrid, Toronto, Ontario, Canada.
Employment Type: Contract
About the Role
We’re seeking a passionate and skilled Machine Learning Engineer to join our client’s growing AI team. In this role, you’ll be responsible for creating machine-learning models and utilizing data to solve complex business problems. You’ll work closely with stakeholders to understand objectives, develop models to meet them and track progress through meaningful metrics.
If you have strong hands-on experience with deep learning frameworks, NLP models like BERT, and a keen eye for data-driven insights.
Key Responsibilities
Design, develop, and deploy machine learning and deep learning systems.
Implement appropriate ML algorithms to address business problems.
Run ML experiments, analyze results, and continuously optimize models.
Perform thorough data exploration, visualization, and preprocessing.
Ensure data quality through validation, cleaning, and acquisition processes.
Manage resources (hardware, data, personnel) to ensure timely project delivery.
Define validation strategies and feature engineering techniques.
Collaborate with cross-functional teams to align models with business objectives.
Required Skills & Experience a must
Deep Understanding of ML Concepts
Strong foundation in machine learning algorithms, statistical modeling, and evaluation techniques.
Expertise in NLP
Hands-on experience with transformer-based models like BERT, for tasks such as text classification, sentiment analysis, and language understanding.
Proficiency with Deep Learning Frameworks
Extensive experience with TensorFlow and/or PyTorch — including model training, fine-tuning, and deployment.
Data Preprocessing and Programming
Proficiency in Python and libraries such as NumPy, Pandas, and Scikit-learn. Strong skills in text preprocessing, tokenization, and working with word embeddings.
Model Optimization & Tuning
Experience in hyperparameter tuning, performance optimization, and managing trade-offs between model complexity and accuracy.
Transfer Learning
Knowledge of leveraging and adapting pre-trained models (especially BERT) for custom applications and datasets.
General Qualifications
Background in statistics, computer science, or related field.
Experience with data quality assurance and validation.
Strong problem-solving, organizational, and communication skills.
A collaborative team player with a proven track record of meeting deadlines and managing priorities in a fast-paced environment.