Staff Applied ML Engineer

 

Job Title:

Staff Applied Machine Learning Engineer (AI Workflows & Product Intelligence)

Why Kaseya?

Join a fast-growing company that’s transforming the IT industry. At Kaseya, you’ll have the opportunity to work with cutting-edge technology, collaborate with a dynamic team, and develop your career in a highimpact role.

Join the Kaseya growth rocket ship and see how we are #ChangingLives!

Job Summary

We’re hiring a Staff Applied Machine Learning Engineer to build AI-powered workflows and data-driven product capabilities across Kaseya’s product suite. This role focuses on applied ML, data analysis, model development, and production integration of AI features that help classify requests, recommend actions, route work, enrich data, and automate repetitive workflows. You’ll partner with Product, Engineering, Data, and ML teams to deliver production-ready AI capabilities while helping teams adopt repeatable patterns for model evaluation, workflow design, and responsible AI usage.

Roles & Responsibilities

• 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

Required Qualifications

• 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

Preferred Qualifications

• 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

Compensation Range

The base salary range for this job is CAD $220,000 – $280,000 / year.

An individual’s base pay depends on various factors including geographical location and review of experience, knowledge, skills, and abilities of the applicant. At Kaseya, certain roles are eligible for benefits and additional rewards. These rewards are allocated based on individual impact in role. In addition, certain roles also have the opportunity to earn sales incentives based on revenue or utilization, depending on the terms of the plan and the employee’s role.

Additional Information

Kaseya provides equal employment opportunity to all employees and applicants without regard to race, religion, age, ancestry, gender, sex, sexual orientation, national origin, citizenship status, physical or mental disability, veteran status, marital status, or any other characteristic protected by applicable law.

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