Staff Applied ML Engineer kaseya · Remote · Canada · Machine Learning Engineering CA$220,000-CA$280,000 4mo ago

About Kaseya

Kaseya is the leading provider of AI-powered IT management and cybersecurity software, serving Managed Service Providers (MSPs) and internal IT organizations worldwide. Our comprehensive platform helps organizations efficiently manage, secure, and automate their IT environments, driving operational efficiency and long-term business success.

Backed by Insight Partners , a leading global software investor, Kaseya has experienced sustained double-digit growth and continues to expand its global footprint. Today, Kaseya supports customers in more than 20 countries and manages over 15 million endpoints worldwide.

Founded in 2000, Kaseya was built by builders - and we're still building. We look for people who create rather than wait, who see a hard problem and lean in, and who treat challenges as raw material. At Kaseya, everyone plays a role in shaping the future of IT: whether you're in engineering, product, sales, marketing, customer support, or operations, your work helps protect, defend, and optimize IT environments across the globe.

We're building teams that grow, perform, and make an impact. If you're driven by the itch to make things better - a product, a process, a career - you'll fit right in.

At Kaseya, we don't just raise the bar. We build it.

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 featur