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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Machine Learning Engineer - **Company:** Vail Resorts - **Location:** Vail, CO, United States (Remote available) - **Salary:** $140,000.0 - $185,000.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Cloud Computing, Cloud Database, Code Review, Continuous Integration, Data Validation, Information Engineering, Database Queries, DevOps, Python (Programming Language), Machine Learning, Azure Machine Learning, Software Engineering, SQL Databases, Software Technical Review, Management of Software Versions, Workflow Management Systems, Large Language Models, Apache Spark, Software Application Programming, Git, Data Lakes, Information Technology, Machine Learning Operations, Data Pipelines, Databricks - **Published:** September 13, 2026 - **Apply:** https://www.careerjet.com/job/us9c919913ff4dd07548b6a428302ec1ae/eaa ## About the Role * 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 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. Preferred qualifications: * 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 ## Description 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., * 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)