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

Principal Machine Learning Engineer