About this opportunity
Vail Resorts lists this Principal Machine Learning Engineer (Python) opportunity in madison, Wisconsin. Review the employer’s description below for duties, qualifications and application requirements.
Job description
We own and operate the most renowned destination resorts in the world as well as regional and local ski areas outside major cities, and connect them all through one unrivaled network. Applications will be accepted up to 90 days after the posting date, or until the position is filled (whichever is first).
We are looking for a curious, driven, innovative machine learning engineer who takes initiative to solve problems and create environments that accelerate the development, deployment, and usage of data science models and AI to drive greater organizational impact.
The Data Science & Data Engineering team within the Enterprise Analytics organization builds data assets, predictive models, analytical applications, and platforms across the organization. Starting Wage: $140,000 - $185,000 + Annual Bonus
~ Shift Type: Full Time hours
~ Housing Availability: 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.
Quantitative Foundation
: B.S. degree in a quantitative field (e.g., Computer Science, Mathematics, Statistics, Economics, Operations Research, Engineering).
Software Engineering Fundamentals:
write clean, modular, testable, maintainable code and understand how to structure production-grade systems rather than one-off notebooks or scripts.
Python and SQL Proficiency:
strong in Python and SQL for building data pipelines, automation, model integrations, analytical workflows, and production services.
Data Modeling and Pipeline Design:
understand how to design reliable, well-structured data assets, including curated tables, feature datasets, batch pipelines, orchestration, data quality checks, and lineage.
ML Lifecycle Fluency:
understand the full model lifecycle: data collection, exploration, model development, validation, deployment, monitoring, retraining, and retirement.
Production ML Patterns:
understand core MLOps patterns such as model registries, feature/data versioning, reproducible environments, testing/validation, monitoring, and rollback.
Cloud and Platform Engineering:
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.
Databricks Expertise
: 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.
DevOps Practices:
You use modern engineering practices such as Git, CI/CD, automated testing, code review, dependency management, environment management, and observability.
Application Development
: You can build applications, APIs, dashboards, or workflow tools that sit on top of data and model outputs.
Ownership
: take responsibility to proactively advance projects, contribute to the organization, and develop the best solutions.
Communication:
explain technical concepts, risks, tradeoffs, and recommendations clearly to technical and non-technical audiences.
Collaboration:
work effectively cross-functionally with data scientists, data engineers, analysts, application engineers, product partners, and business stakeholders.
Pragmatism
: 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
The expected Total Compensation for this role is $140,000 - $185,000 + Annual Bonus. MORE employee discounts on lodging, food, gear, and mountain shuttles
~401(k) Retirement Plan
~ Employee Assistance Program
~ Excellent training and professional development
Full Time roles are eligible for the above, plus:
Health Insurance; Medical Insurance, Dental Insurance, and Vision Insurance plans (for eligible seasonal employees after working 500 hours)
Employees can work remotely from British Columbia, Washington D.Please note that the ability to work in person or off-site, and the particulars related to such work, are subject to change at any time; and, accordingly, the Company reserves the right to change its policies and/or require in-person/in-office work or off-site work at any time in its sole discretion.
In completing this application, and when submitting related documentation, applicants may redact information that identifies their age, date of birth, and/or dates of attendance at or graduation from an educational institution.
We follow all federal, state, and local laws including restrictions on child/minor labor. Minors hired into this position will not be asked or permitted to engage in any activities restricted to adult workers.
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, protected veteran status or any other status protected by applicable law.
Job Code Function: Data Science
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.