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Machine Learning Engineer

1 month, 3 weeks ago
Canada
Full-time
Mid Level
Machine Learning Engineer
Software Development
Apache Airflow Apache Spark AWS Databricks Kubeflow Machine Learning MLflow MLOps Python Terraform
Apply Now
Wave

Wave

Wave is a small business software company that offers a suite of money management tools including invoicing, accounting, credit card processing, payroll, receipt scanning, and personal finance tools. With 2.5 million customers worldwide, Wave provides ...

Internet Software & Services
251-1K
Founded 2010
$80M raised
View All Jobs 6

Description

  • Develop, train, and deploy machine learning models into production environments.
  • Build robust and scalable machine learning pipelines and platforms.
  • Champion best practices across coding, testing, and MLOps workflows.
  • Optimize ML and AI use cases for reliability, scalability, and cost efficiency.
  • Partner with cross-functional stakeholders to translate business needs into technical specifications.
  • Integrate ML features into live applications and production systems.
  • Establish controls for model dependability, fairness, compliance, lineage, and data protection.
  • Build observability and monitoring systems to track model health and operational metrics.

Requirements

  • 3–5 years of professional experience in machine learning engineering with production deployment experience.
  • Deep understanding of the modern data stack, including data ingestion workflows and curated data warehouses like Databricks or Redshift.
  • At least 3 years of hands-on experience with AWS infrastructure, including SageMaker, Spark/AWS Glue, and Terraform.
  • High proficiency with Airflow or similar orchestration systems for multi-stage workflow automation.
  • Practical experience with MLflow, Kubeflow, or SageMaker Feature Store across the ML lifecycle.
  • Familiarity with model governance practices, including lineage, fairness, and privacy, and with data cataloging tools for compliance.
  • Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction.
  • Experience in FinTech or Financial Risk environments is a significant advantage.

Benefits

  • Inclusive and accessible candidate experience with accommodations available during the recruitment process.
  • Opportunity to work in a flexible environment with support for being connected and successful wherever you work.
  • A creative, collaborative workplace culture.
  • Use of Google Gemini during interviews for confidential note-taking, helping interviewers stay focused.

Interested in this position?

Apply directly on the company website

Apply Now

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