Principal Machine Learning Engineer

Job Description

Job Description

Our mission is to create the Experience of a Lifetime for our employees, so they can, in turn, create the Experience of a Lifetime for our guests. 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. We are looking for ambitious leaders, innovators and creators to join our talented team. If you're ready to pursue your fullest potential, we want to get to know you Candidates for year-round positions are reviewed on a rolling basis. Applications will be accepted up to 90 days after the posting date, or until the position is filled (whichever is first).

Job Summary:

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.

Our team collaborates with business stakeholders, analysts, and technology teams to tackle high-impact use cases with state-of-the-art models and tools to grow the business, streamline costs, and improve guest experiences.

Job Specifications: 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 Job

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.

Job

Requirements:

Technical

Skills:
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.

System Design: You can reason through tradeoffs across reliability, latency, scale, cost, governance, maintainability, and ease of use.

Soft

Skills:

Curious : bring intellectual curiosity, an inquisitive nature, and a desire to deepen your knowledge and continue learning. Ownership : take responsibility to proactively advance projects, contribute to the organization, and develop the best solutions. Communication: explain technical