Staff Applied ML Engineer

Overview

Staff Applied Machine Learning Engineer to build AI-powered workflows and data-driven product capabilities across Kaseya's product suite, focused on applied ML, data analysis, model development, and production integration of AI features.

What you'll do

• Analyze product and customer data using Python, pandas, SQL, PySpark, or similar tools to identify patterns and modeling opportunities
• Build and productionize ML models for classification, recommendations, similarity, ranking, routing, and prediction use cases
• Design AI-powered workflows that transform unstructured inputs such as tickets, emails, forms, messages, and logs into structured data
• Partner with Engineering teams to integrate ML models and AI workflows into production systems through APIs, services, and automation logic
• Define evaluation methods, telemetry, feedback loops, and monitoring practices for AI-powered product features
• Build reusable patterns, templates, and best practices for data ingestion, feature creation, model usage, and responsible AI implementation
• Provide technical guidance to product teams on AI opportunity sizing, ML design, model trade-offs, and production readiness
• Mentor junior data and ML engineers through code reviews, model reviews, pairing, and technical coaching

What you'll need

• 5+ years of experience in data science, machine learning engineering, applied ML, or a related production-focused role
• Experience building ML models for classification, recommendation, similarity, ranking, prediction, or related product use cases
• Experience using Python, pandas, SQL, and PyTorch or similar tools for data analysis and model development
• Experience using PySpark or similar distributed data processing frameworks
• Experience integrating ML models into production systems through APIs, microservices, batch workflows, or automated product workflows

Nice to have

• Experience with LLM-powered workflows, RAG, prompt engineering, fine-tuning, tool use, or agent orchestration
• Experience building agent-assist features, automated resolution workflows, or AI-powered operational products
• Experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or similar platforms
• Experience with production model monitoring, model evaluation, online experiments, or feedback loops
• Experience applying matrix factorization, embeddings, clustering, dimensionality reduction, or latent factor modeling
• Experience working in a platform or enablement role supporting multiple product teams
• Experience mentoring data scientists, ML engineers, analysts, or software engineers on applied ML practices

Details

• Location: Canada, Remote
• Base salary range: CAD $220,000 – $280,000 per year, depending on factors including geographical location and review of experience, knowledge, skills, and abilities
• Certain roles are eligible for benefits, additional rewards, and sales incentives based on revenue or utilization, depending on plan terms and role