Join Vail Resorts Corporate’s Enterprise Analytics team and help take machine learning from prototype to production across a hybrid, collaborative environment in the United States. This Principal Machine Learning Engineer role blends practical MLOps with scalable data foundations, Databricks platform patterns, and mentorship for teams building model-powered workflows. Compensation includes USD 140,000 - 185,000 per year plus an annual bonus, with year-round full-time hours.
What you’ll do
- Productionize machine learning models created by data science teams into monitored, reliable, maintainable systems.
- Build trustworthy, scalable model foundations that support training, inference, monitoring, and analytics data.
- Architect ML platform patterns in Databricks that improve reliability, consistency, governance, performance, and cost discipline across ML and data workflows.
- Scope high-impact ML engineering opportunities across the business based on where engineering investment will drive outcomes.
- Create reusable assets including tools, libraries, standards, documentation, and production-readiness practices that help data science and data engineering teams move faster.
- 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.
Key requirements
- B.S. degree in a quantitative field (examples include Computer Science, Mathematics, Statistics, Economics, Operations Research, or Engineering).
- Ability to write clean, modular, testable, maintainable code and structure production-grade systems rather than one-off notebooks or scripts.
- Strong Python and SQL for data pipelines, automation, model integrations, analytical workflows, and production services.
- Understanding of reliable, well-structured data assets including curated tables, feature datasets, batch pipelines, orchestration, data quality checks, and lineage.
- Knowledge of the full model lifecycle: data collection, exploration, model development, validation, deployment, monitoring, retraining, and retirement.
- Comfort with core MLOps patterns such as model registries, feature/data versioning, reproducible environments, testing/validation, monitoring, and rollback.
- Experience working in cloud-based data and ML environments, including permission foundations, environments, jobs, services, storage, networking, and cost-aware architecture.
- Familiarity with Databricks and related components including Spark, Unity Catalog, Delta Lake, Databricks Workflows, MLflow, model registry patterns, job/cluster optimization, and governance.
- Use of modern engineering practices: Git, CI/CD, automated testing, code review, dependency management, environment management, and observability.
- Experience building applications, APIs, dashboards, or workflow tools on top of data and model outputs.
- Ability to reason through tradeoffs across reliability, latency, scale, cost, governance, maintainability, and ease of use.
- Traits that support long-term impact: curiosity and continuous learning, ownership, clear communication to technical and non-technical audiences, strong cross-functional collaboration, and practical judgment to balance architecture with delivery needs.
Tools and technologies
Python, SQL, Databricks, Spark, Unity Catalog, Delta Lake, Databricks Workflows, MLflow, Git, CI/CD, LLM, agent-based systems
Benefits
- Ski/Mountain Perks! Free passes for employees, employee discounted lift tickets for friends and family, and free ski lessons
- MORE employee discounts on lodging, food, gear, and mountain shuttles
- 401(k) Retirement Plan
- Employee Assistance Program
- Excellent training and professional development
- Health Insurance: Medical, Dental, and Vision plans (for eligible seasonal employees after working 500 hours)
- Free ski passes for dependents
- Critical Illness and Accident plans
Preferred qualifications
- Graduate degree (Masters or PhD) in a quantitative field
- Experience with dbt (Core) for modular data modeling, including testing, documentation, and dependency management
- Experience building and monitoring agentic solutions as an AI engineer
Role details
- Starting Wage: $140,000 - $185,000 + Annual Bonus
- Employment Type: Year Round
- Shift Type: Full Time hours
- Minimum Age: At least 18 years of age
- Housing Availability: No
Employees can work remotely from British Columbia, Washington D.C., and the following 16 U.S. states: California, Colorado, Indiana, Michigan, Minnesota, Missouri, New Hampshire, New York, Nevada, Ohio, Pennsylvania, Utah, Vermont, Washington State, Wisconsin, Wyoming.
Requisition ID: 517322
Reference Date: 09/05/2026
Job Code Function: Data Science
