Principal Machine Learning Engineer

Vail Resorts operates destination resorts and ski areas connected through a global network. The company is seeking a Principal Machine Learning Engineer to productionize machine learning models, build scalable data foundations and Databricks platform patterns, and develop model-powered applications. The role also provides technical leadership and prepares the organization for future AI engineering, including LLM and agent-based systems.

Responsibilities:

• Productionize ML models developed by data science into reliable, monitored, maintainable systems
• Build model data foundations that ensure training, inference, monitoring, and analytics data are trustworthy and scalable
• Architect ML platform patterns in Databricks that bring reliability, consistency, governance, performance, and cost discipline to ML and data workflows
• Identify and scope opportunities for ML engineering across the business for high-impact
• Develop reusable tools, libraries, standards, documentation, and production-readiness practices to enable data science and data engineering teams
• Develop analytical and model-powered applications that turn data and ML outputs into usable business workflows for end users
• Prepare the platform for future AI engineering, including LLM and agent-based systems, as the organization matures
• Provide technical leadership and mentoring across engineering, architecture, and development including design and code reviews

Requirements:

• B.S. degree in a quantitative field (e.g., Computer Science, Mathematics, Statistics, Economics, Operations Research, Engineering)
• write clean, modular, testable, maintainable code and understand how to structure production-grade systems rather than one-off notebooks or scripts
• strong in Python and SQL for building data pipelines, automation, model integrations, analytical workflows, and production services
• understand how to design reliable, well-structured data assets, including curated tables, feature datasets, batch pipelines, orchestration, data quality checks, and lineage
• understand the full model lifecycle: data collection, exploration, model development, validation, deployment, monitoring, retraining, and retirement
• understand core MLOps patterns such as model registries, feature/data versioning, reproducible environments, testing/validation, monitoring, and rollback
• You are comfortable working in cloud-based data and ML environments and understand the foundations of permissions, environments, jobs, services, storage, networking, and cost-aware architecture
• You're familiar and experienced with the core parts of Spark, Unity Catalog, Delta Lake, Databricks Workflows, MLflow, model registry patterns, job/cluster optimization, and governance
• You use modern engineering practices such as Git, CI/CD, automated testing, code review, dependency management, environment management, and observability
• You can build applications, APIs, dashboards, or workflow tools that sit on top of data and model outputs
• You can reason through tradeoffs across reliability, latency, scale, cost, governance, maintainability, and ease of use
• bring intellectual curiosity, an inquisitive nature, and a desire to deepen your knowledge and continue learning
• take responsibility to proactively advance projects, contribute to the organization, and develop the best solutions
• explain technical concepts, risks, tradeoffs, and recommendations clearly to technical and non-technical audiences
• work effectively cross-functionally with data scientists, data engineers, analysts, application engineers, product partners, and business stakeholders
• You know how to balance ideal architecture with business urgency, team maturity, operational constraints, and the need to ship
• A graduate degree (Masters or PhD) in a quantitative field
• Experience with dbt (Core) for modular data modeling, including testing, documentation, and dependency management
• Experience with AI engineer to use, build, and monitor agentic solutions