Machine Learning Engineer – LMIA / Tier 2 Sponsorship Supported

🏢 Teck Resources Limited📍 Trail, British Columbia, Canada💼 Full-Time💻 On-site🏭 Mining & Metals💰 90000-130000 per year

About the Company

Teck Resources Limited is one of Canada’s leading diversified resource companies, committed to responsible mining and mineral development with major business units focused on steelmaking coal, copper, zinc, and energy. We are leveraging technology and innovation to drive efficiency and sustainability across our operations. Join a team where your expertise in machine learning will directly contribute to optimizing processes, enhancing safety, and making a tangible impact on our core business.

Job Description

We are seeking a talented and driven Machine Learning Engineer to join our innovative technology team. This is a unique opportunity for individuals who require LMIA or Tier 2 sponsorship to work in Canada, as Teck Resources is prepared to support the right candidate through the immigration process. You will be instrumental in designing, developing, and deploying robust machine learning models to solve complex challenges in areas such as predictive maintenance, operational efficiency, resource optimization, and data analysis within our industrial environment.

Key Responsibilities

  • Design, develop, and implement machine learning models and algorithms.
  • Clean, preprocess, and transform large datasets for model training and evaluation.
  • Evaluate model performance, identify areas for improvement, and optimize algorithms for accuracy and efficiency.
  • Deploy ML models into production systems and ensure their reliability and scalability.
  • Collaborate with data scientists, software engineers, and domain experts to understand requirements and deliver impactful solutions.
  • Monitor deployed models for performance degradation and retrain as necessary.
  • Stay current with the latest advancements in machine learning and artificial intelligence.
  • Document model architecture, methodologies, and results clearly and comprehensively.

Required Skills

  • Proficiency in Python and relevant ML libraries (TensorFlow, PyTorch, scikit-learn).
  • Strong understanding of machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning).
  • Experience with data preprocessing, feature engineering, and model evaluation techniques.
  • Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and MLOps practices.
  • Solid understanding of software engineering principles and best practices.
  • Ability to work with large, complex datasets and distributed computing frameworks (e.g., Spark).
  • Excellent problem-solving and analytical skills.
  • Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.

Preferred Qualifications

  • Master's or Ph.D. in a relevant technical field.
  • Experience with time series analysis and predictive modeling in an industrial context.
  • Familiarity with containerization technologies (Docker, Kubernetes).
  • Experience with SQL and NoSQL databases.
  • Previous experience in the mining, metals, or heavy industry sectors.
  • Demonstrated ability to deliver end-to-end machine learning projects.

Perks & Benefits

  • Comprehensive health and dental benefits.
  • Retirement savings plan with company contributions.
  • Paid vacation and holidays.
  • Opportunities for professional development and continuous learning.
  • Relocation assistance may be available.
  • Support for LMIA / Tier 2 Sponsorship for qualified international candidates.
  • Employee assistance program.
  • Dynamic and collaborative work environment.
  • Impactful work contributing to sustainable resource development.

How to Apply

If you are interested in this position, please click the "Apply Now" button below. To ensure your application is properly considered, please prepare the following:

  • An up-to-date Resume or CV
  • A brief cover letter summarizing your experience and motivation

Applications are reviewed on a rolling basis. Only shortlisted candidates will be contacted for an interview.

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