RQ09177 - Machine Learning Engineer - Senior

Rubicon Path; Toronto; On-site; CAD 100,000 - 130,000; Full time

Job summary

A technology firm in Toronto is seeking a Senior Machine Learning Engineer. The role involves creating and deploying machine learning models to meet business objectives. The ideal candidate will have deep knowledge of machine learning concepts, proficiency in frameworks like TensorFlow and PyTorch, and strong programming skills in Python. This position requires experience in natural language processing and data preprocessing, offering a competitive salary and growth opportunities.

Qualifications

• Proficiency in fundamental machine learning concepts, algorithms, and techniques.
• Knowledge of NLP techniques and models, especially BERT and other transformer-based models.
• Experience with implementing, training, and fine-tuning BERT models.
• Ability to perform text preprocessing, tokenization, and understanding of word embeddings.
• Strong programming skills in Python.
• Skills in optimizing model performance.
• Understanding of how to leverage pre-trained models.

Responsibilities

• Create machine learning models and utilize data to train them.
• Analyze data to find relations between inputs and outputs.
• Develop models that help achieve business objectives.
• Design and develop machine learning and deep learning systems.
• Run machine learning tests and experiments.

Skills

Machine Learning Concepts

Natural Language Processing

Deep Learning Frameworks

Data Preprocessing

Programming in Python

Model Optimization

Transfer Learning

Tools

TensorFlow

PyTorch

NumPy

Pandas

Scikit-learn

Job description

About the job RQ09177 - Machine Learning Engineer - Senior

Description:

Responsibilities: Creates machine learning models and utilizes data to train models Focuses on analyzing data to find relations between the input and the desired output Understands business objectives and develops models that help achieve them, along with metrics to track their progress Designs and develops machine learning and deep learning systems Runs machine learning tests and experiments Implements appropriate machine learning algorithms General Skills: Experience managing available resources such as hardware, data, and personnel so that deadlines are met Experience analyzing the machine learning algorithms that could be used to solve a given problem and ranking them by their success probability Experience exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world Experience verifying data quality, and/or ensuring it via data cleaning Experience supervising the data acquisition process if more data is needed Experience finding available datasets online that could be used for training Experience defining validation strategies Experience defining the preprocessing or feature engineering to be done on a given dataset Background in statistics and computer programming A team player with a track record for meeting deadlines, managing competing priorities and client relationship management experienceSkillsExperience 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.

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.