Machine Learning Engineer
Summary
Build, deploy, and scale AI/ML models and pipelines for Wave’s fintech platform, ensuring reliability, governance, and integration with AWS and MLOps tools.
Here's How You Make an Impact:
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Develop & Deploy: Focus on the hands-on building, training, and operational deployment of machine learning models, ensuring they perform reliably within existing production environments.
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Champion Technical Standards: Advocate for top-tier practices across coding, testing, and MLOps processes. Navigate ambiguity autonomously to refine pipelines and elevate ML engineering workflows.
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Optimize & Scale: Construct resilient, cost-efficient ML & AI use cases. Balance sustaining established models with accelerating the rollout of highly scalable, modern systems.
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Partner & Collaborate: Team up with cross-functional stakeholders, including risk specialists, product leads, and software developers, to convert strategic needs into technical specs and smoothly embed ML features into live applications.
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Establish Controls & Governance: Uphold stringent benchmarks for model dependability, fairness, and compliance. Direct the integration of lineage tracking and data protection workflows into our automated systems.
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Track & Evaluate: Formulate comprehensive observability systems to capture model health and key operational metrics, ensuring machine learning investments yield quantifiable organizational value.
You Thrive Here By Possessing the Following:
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Experience: Minimum of 3–5 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments.
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Technical Depth: Deep understanding of the modern data stack, including data ingestion workflows and experience working with curated data warehouses like Databricks or Redshift.
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Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC), Terraform.
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Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles.
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MLOps Toolkit: Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle.
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Governance Mindset: Familiarity with model governance practices (lineage, fairness, and privacy) and experience using data cataloging tools for compliance.
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Communication: Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction.
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Industry Context: Experience in FinTech or Financial Risk environments is a significant advantage.
Skills
As published by lever · 9 questions · 2 written answers
Basics
Resume/CV, Full name, Pronouns, Email, Phone, Current location, Current company, LinkedIn URL, Twitter URL, GitHub URL, Portfolio URL, Other website
Short answers (2)
- Full name
- If you were referred by a Wave employee, please provide the name of the person who referred you here. optional
Pick from a list (5)
- Are you located in Canada?
- Are you legally eligible to work in Canada with no restrictions (e.g. PR, open work permit, etc.)?
- Have you reviewed the posted salary range and are your expectations aligned with it? optional
- Prior to the live manager interview, all candidates are required to complete a secure identity verification. Please agree to this below.
- Every Wave hire is required to complete and clear a criminal check so that Wave can remain PCI compliant as an organization. By submitting this application, you acknowledge that, if hired by Wave, you agree to complete background checks, and you don't foresee any concerns in regards to obtaining positive results. optional
Written answers (2)
- What is your desired salary? (Please write down a number)
- If you require any type of accommodation during your interview process and/or while performing the role described above, please specify it here so that we can do our best to support you. optional