Kaseya
IT management and cybersecurity software
SeniorStaffAppliedMLEngineer
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“Senior Staff Applied ML Engineer at Kaseya. Skills: Applied ML Engineering, Data Analysis, ML Modeling, AI-Powered Workflows, Production ML, Technical Leadership, Mentoring. Explore and analyze data using Python, pandas, and PySpark. Use matrix factorization, clustering, dimensionality reduction, and related techniques”
What You'll Achieve.
driving operational efficiency and long-term business success; accelerate teams; deliver exceptional outcomes for our customers and teammates
Industry & Context.
translate business problems into data/ML projects; handle the most complex modeling or workflow automation pieces when teams get stuck; develop good intuitions about metrics, evaluation, and operational reliability
What They're Looking For.
Must Have
5+ years in data science, ML engineering, or a similar applied role, record of shipping production data/ML features, Python skills and experience with pandas for data analysis, Experience with PySpark or other distributed data processing frameworks, Solid understanding of ML fundamentals, including: Supervised learning and classification models, Matrix factorization / embeddings / latent factor models, Feature engineering and model evaluation (offline metrics and online experiments), Proficiency with PyTorch (or a similar deep learning framework) and related ML tooling, 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, Experience serving as a technical lead or senior individual contributor across multiple teams or projects, Proven ability to translate business problems into data/ML projects, and to clearly explain tradeoffs to non-ML stakeholders, Track record of mentoring junior engineers/analysts and improving team practices (e. g. , review culture, testing, monitoring), communication skills and the ability to drive alignment across product, engineering, and operations
Nice to Have
Experience with LLMs and language-centric workflows (RAG, prompt engineering, fine-tuning, tool/agent orchestration), Experience building agent-assist features or automated workflows in operational or customer-facing products, Familiarity with MLOps tools (e. g. , MLflow, Kubeflow, SageMaker, Vertex, etc. ) and production model monitoring, Prior experience in a platform/enablement role, supporting many product teams with shared data and ML capabilities
What You'll Do.
Explore and analyze data using Python
Use matrix factorization
dimensionality reduction
and related techniques
and productionize ML models for categorization
and other prediction or ranking tasks
Design and implement AI-driven ingest flows
Build workflows where AI can auto-fill or suggest key fields
proactively ask for missing information
surface similar past items
and fully handle simple requests
Work closely with engineers to integrate models and workflows into production systems
Help product teams identify and scope AI opportunities
and best practices for data ingestion
Provide design and architecture guidance on data and ML-heavy features
Handle complex modeling or workflow automation pieces
Mentor and guide junior data/ML engineers and analysts
Conduct code and model reviews
Pair with junior engineers on tricky problems
Help junior engineers develop good intuitions about metrics
and operational reliability
Establish and socialize standards for experimentation
and responsible AI usage
How You'll Work.
Team & Collaboration
Partner with multiple product teams to extract insights from data and build AI-powered features and automated workflows; Enable product teams by teaching, coaching, and guiding them on data and ML best practices; Lead by example, doing complex data analysis and ML modeling, architecture, and implementation work to accelerate teams while mentoring more junior data/ML folks; Work closely with engineers to integrate models and workflows into production systems; Work with multiple product teams to help them identify and scope AI opportunities; Serve as a trusted advisor and technical lead; Join projects to handle the most complex modeling or workflow automation pieces when teams get stuck; Mentor and guide junior data/ML engineers and analysts; Drive alignment across product, engineering, and operations
Communication Scope
clearly explain tradeoffs to non-ML stakeholders; communication skills and the ability to drive alignment across product, engineering, and operations
Full Job Description
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 Data & ML Modeling · Explore and analyze data using Python, pandas, and PySpark (or similar tools). · Use matrix factorization, clustering, dimensionality reduction, and related techniques to understand and prepare data for modeling, and to identify and label latent factors (e.g., user behavior pa
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