Machine Learning Engineer

🕒 August 12

🇨🇦 Canada – Remote

💵 $100k - $130k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 3%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Wave HQ

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2010

💼 Consulting

📣 Marketing

💸 Finance

Consulting • Marketing • Finance

Wave HQ is a financial services company that provides a suite of money management tools designed to help small business owners. The platform offers features such as invoicing, online payments, accounting, and payroll, all in one integrated system. Wave HQ aims to simplify the financial management process for small business owners, enabling them to manage invoices, track income and expenses, and process payroll efficiently. The company is targeted towards freelancers, contractors, consultants, and self-employed entrepreneurs, providing them with a user-friendly dashboard and access to bookkeeping, accounting, and payroll coaching.

📋 Description

• Design, develop, train, and deploy foundational AI and machine learning models in production environments • Build robust, scalable machine learning pipelines and platforms supporting advanced analytics and business intelligence • Advocate for high standards across coding, testing, and MLOps processes • Construct resilient, cost-efficient ML and AI use cases and scale modern systems • Collaborate with risk specialists, product leads, and software developers to translate strategic needs into technical specifications and embed ML features into live applications • Establish model dependability, fairness, compliance, lineage tracking, and data protection controls • Develop observability systems to capture model health and operational metrics and evaluate organizational value

🎯 Requirements

• Minimum 3–5 years of professional experience in machine learning engineering • Proven track record of deploying machine learning models into production environments • Deep understanding of the modern data stack and data ingestion workflows • Experience with 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 • Practical experience with MLflow, Kubeflow, or SageMaker Feature Store • Familiarity with model governance practices, including lineage, fairness, and privacy • Experience using data cataloging tools for compliance • Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction • FinTech or Financial Risk experience is a significant advantage

🏖️ Benefits

• Bonus Structure • Employer-paid Benefits Plan • Health & Wellness Flex Account • Wellness Days • Paid Holiday Shutdown • Wave Days (extra vacation days in the summer)

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