Posted May 14, 2026
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 has built a culture centered around innovation, accountability, and results. We are a high-growth, high-performance organization that values individuals who are driven, adaptable, and committed to delivering exceptional outcomes for our customers and teammates alike. At Kaseya, success comes from embracing challenges, moving with urgency, and continuously raising the bar. Overview
We’re hiring Applied ML Engineers to partner with multiple product teams to extract insights from data and build AI-powered features and automated workflows across the product suite. In this role, you will both:
· Enable product teams: teach, coach, and guide them on data and ML best practices
· Lead by example: do complex data analysis and ML modeling, architecture, and implementation work as needed to accelerate teams while mentoring more junior data/ML folks. You’ll own the data analysis, ML modeling, and workflow logic that let AI understand user requests, enrich and route them, suggest actions, and in some cases fully automate resolution. What You’ll Do
Core Skills
· 5+ years in data science, ML engineering, or a similar applied role, with a strong record of shipping production data/ML features. · Strong Python skills and experience with pandas for data analysis. · Experience with PySpark or other distributed data processing frameworks. · Solid understanding of ML fundamentals, including:
o Supervised learning and classification models
o Matrix factorization / embeddings / latent factor models
o Feature engineering and model evaluation (offline metrics and online experiments)
· Proficiency with PyTorch (or a similar deep learning framework) and related ML tooling. · Strong SQL and experience with modern data warehouses / data lakes. · Comfort working with APIs, microservices, and production integration of ML models, including performance and reliability considerations.
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