Job Description
Team: IT
This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning Engineer in Canada.
This role focuses on building and deploying applied machine learning systems that directly improve high-impact customer operations such as disputes, refunds, fraud detection, and chargeback resolution. You will work at the intersection of ML engineering, product, and operations to design intelligent systems that automate complex decision-making workflows. The environment is highly collaborative and production-oriented, requiring you to take models from experimentation to scalable real-world deployment. You will also contribute to building LLM-powered pipelines that extract and structure information from unstructured data to support faster and more accurate outcomes. The role offers exposure to both classical ML and modern AI/LLM systems, with a strong emphasis on measurement, monitoring, and reliability. This is an opportunity to build production-grade ML systems that have direct financial and customer impact at scale.
Accountabilities:
- Develop and deploy machine learning models to automate customer operations such as disputes, refunds, fraud detection, and chargeback handling.
- Build and maintain production-grade ML pipelines, including training, evaluation, deployment, and monitoring workflows.
- Design and implement LLM-based systems for structured data extraction from unstructured sources such as documents and images.
- Run offline experiments and prototyping efforts to identify optimal modeling approaches and production-ready solutions.
- Collaborate with Engineering, Product, and Operations teams to define requirements, evaluate trade-offs, and deliver scalable ML solutions.
- Ensure model performance, reliability, and risk controls through robust measurement and monitoring frameworks.
- Contribute to improving system architecture and ML lifecycle tooling across teams.
- 2+ years of experience as a Machine Learning Engineer or in a closely related role.
- Strong proficiency in Python and experience writing production-quality, well-tested code.
- Experience building and evaluating tabular ML models, preferably using gradient-boosted decision trees (e.g., XGBoost, LightGBM, CatBoost).
- Hands-on experience with LLM APIs (OpenAI, Anthropic, or similar), including prompt engineering and structured extraction workflows.
- Familiarity with unstructured data processing such as document parsing, OCR, or image/text extraction.
- Experience with ML lifecycle tools such as Airflow, MLflow, Kubeflow, or equivalent platforms.
- Ability to design end-to-end ML solutions across multiple system components with strong software engineering practices.
- Experience debugging complex systems and reviewing code in large production codebases.
- Strong communication skills for collaboration with technical and non-technical stakeholders.
- Demonstrated ownership mindset and commitment to continuous learning and improvement.
- Competitive base salary with additional equity and performance-based rewards.
- Comprehensive health, dental, and vision coverage fully or highly subsidized.
- Flexible remote-first work model across Canada.
- Wellness, technology, and lifestyle spending allowances.
- Generous paid time off and parental leave policies.
- Employee stock purchase plan with discounted equity opportunities.
- Strong engineering culture focused on applied AI innovation and real-world impact.
- Access to modern ML and LLM tooling in production-scale environments.
Requirements:
Benefits:
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Date Posted
05/27/2026
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