Kaseya - Senior Staff Applied ML Engineer
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Requirements
• 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: • 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. • 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. • Leadership & Collaboration • 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). • Strong communication skills and the ability to drive alignment across product, engineering, and operations. • 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. • Additional informationKaseya 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.
Responsibilities
• Enable product teams: teach, coach, and guide them on data and ML best practices • Enable • 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. • Lead by example • 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. • Data & ML Modeling • 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 patterns, content/topic clusters, expertise dimensions). • Create, tune, and productionize ML models for: • Categorization / classification • Recommendations and similarity • Other prediction or ranking tasks that power product features • AI-Powered Workflows & Features • Design and implement AI-driven ingest flows that turn unstructured inputs (tickets, emails, forms, messages, logs, etc.) into well-structured data that models and downstream systems can use. • Build workflows where AI can: • Auto-fill or suggest key fields and metadata. • Proactively ask users/customers for missing or ambiguous information (e.g., via email or messaging). • Surface similar past items or solutions to assist humans in decision-making. • Fully handle simple, repetitive “Level 1” style requests end-to-end when safe to do so. • Work closely with engineers to integrate models and workflows into production systems with proper monitoring, fallbacks, and guardrails. • Cross-Team Leadership & Enablement • Work with multiple product teams to help them identify and scope AI opportunities in their areas. • Define patterns, templates, and best practices for data ingestion, feature creation, model usage, and evaluation that teams can reuse. • Serve as a trusted advisor and technical lead: • Provide design and architecture guidance on data and ML-heavy features. • Mentor and guide junior data/ML engineers and analysts: • Conduct code and model reviews. • Pair with them on tricky problems. • Help them develop good intuitions about metrics, evaluation, and operational reliability. • Help establish and socialize standards for experimentation, documentation, and responsible AI usage across teams.
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