Candidates MUST be authorized to work in Canada / hold a valid work visa. CCI does not sponsor work visas.
We are seeking a Machine Learning Engineer with strong NLP expertise to design, develop, and optimize machine learning and deep learning models that support business objectives. The role focuses on building data-driven solutions, running experiments, and deploying high-performance NLP models—particularly transformer-based architectures such as BERT —in a production-oriented environment.
Mandatory Skills & Experience:
- Deep understanding of machine learning concepts, algorithms, and techniques
- Strong expertise in Natural Language Processing (NLP) , including BERT and transformer-based models
- Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch
- Strong Python programming skills , including NumPy, Pandas, and Scikit-learn
- Proven ability in data preprocessing , text tokenization, and working with word embeddings
- Experience with model optimization, tuning, and performance trade-off analysis
- Solid understanding of transfer learning and adapting pre-trained models to custom datasets
Key Responsibilities:
- Design, develop, and implement machine learning and deep learning models
- Analyze data to identify relationships between inputs and desired outputs
- Translate business objectives into measurable ML solutions and success metrics
- Run ML experiments, validate results, and refine models for performance
- Implement and evaluate appropriate ML algorithms based on problem context
- Apply feature engineering and preprocessing strategies to improve model accuracy
- Ensure data quality through validation, cleaning, and preprocessing
- Support or oversee data acquisition and sourcing of external datasets when required
Experience and Skill Set Requirements:
- (15%) 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.
- (20%) 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.
- (30%)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.
- (20%) Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.
- (15%) Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.
