Machine Learning Engineer | Remote

Crossing Hurdles; Canada; On-site; CAD 90,000 - 120,000; Full time

Job summary

Crossing Hurdles is seeking an experienced machine learning engineer to design, develop, and deploy ML models addressing complex business challenges. You will work with cross-functional teams to gather requirements and define data pipelines for AI solutions.

You will implement and optimize ETL processes, use Python and NumPy/pandas/scikit-learn, monitor model performance, and document methodologies for transparency. Remote, distributed collaboration is essential.

Qualifications

• Expert-level proficiency in ML techniques and algorithms.
• Advanced Python and data science libraries (NumPy, pandas, scikit-learn).
• Hands-on experience designing and managing production ETL workflows.
• Strong knowledge of data preprocessing, feature engineering, and metrics.
• Ability to collaborate effectively in remote, distributed teams.

Responsibilities

• Design, develop, and deploy ML models to address business challenges.
• Collaborate with cross-functional teams to gather requirements and deliver AI solutions.
• Implement, optimize, and maintain ETL processes for data ingestion and transformation.
• Monitor and improve model performance and accuracy in production.
• Document processes and model decisions for transparency and reproducibility.

Skills

Machine learning

Python

ETL

Data preprocessing

Feature engineering

Model evaluation

Remote collaboration

Tools

NumPy

pandas

scikit-learn

Job description

• Design, develop, and deploy machine learning models to address complex business challenges.
• Collaborate with cross-functional teams to gather requirements, define data pipelines, and deliver impactful AI solutions.
• Implement, optimize, and maintain ETL processes for efficient data ingestion, transformation, and management.
• Utilize Python and relevant libraries to create clean, efficient, and reusable code for AI and machine learning applications.
• Continuously monitor, evaluate, and enhance the performance and accuracy of deployed models.
• Document processes, methodologies, and model decisions to ensure transparency and reproducibility.

Requirements

• Have expert-level proficiency in machine learning techniques and algorithms.
• Possess advanced programming skills in Python and its data science ecosystem, including libraries such as NumPy, pandas, and scikit-learn.
• Have hands‑on experience designing and managing ETL workflows in production environments.
• Have a strong understanding of data preprocessing, feature engineering, and model evaluation metrics.
• Demonstrate a proven ability to collaborate and thrive in remote, distributed team settings.