Principal Machine Learning Engineer

Vail Resorts is hiring a Principal Machine Learning Engineer to accelerate the development, deployment, and use of data science models and AI. The role builds and productionizes machine learning systems, establishes platform patterns and data foundations, develops model-powered applications, and provides technical leadership across teams.

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