CellarTracker is the world’s most trusted wine community, where technology and passion meet to make wine discovery accessible and exciting for everyone.

Founded in 2003 as a cellar management tool for wine collectors, CellarTracker has since expanded to help millions find and experience wines they love.

Today, across our website and newly reimagined mobile app—which has quickly earned over 2,000 reviews with a 4.9-star rating—our 1 million members contribute to a robust database of 5 million unique wines and over 10 million community reviews.

In just three years, our team has grown from 3 to 20, and we’re looking for more awesome team members to drive our mission forward.We are seeking an Applied Machine Learning Engineer who is passionate about harnessing the power of data and generative AI to build innovative consumer products.

At CellarTracker, we strive to deliver exceptional digital experiences that simplify and enrich the wine journey for our users.

In this role, you will play a critical part in analyzing complex datasets, developing machine learning models, and translating data-driven insights into impactful product features.

We have a fully cloud native stack and work almost exclusively in Azure so some familiarity with Azure AI tools is a plus.As a key member of our team, you'll act as an owner, creating clarity from ambiguity and driving projects from ideation to execution.

You'll collaborate closely with cross-functional teams to uncover insights that inform product development and enhance user experience.

If you're self-motivated, have a bias for action, and value empathy and collaboration, we'd love to hear from you.Responsibilities Data Analysis & Modeling: Analyze large and complex datasets to extract meaningful insights that drive product decisions.

Machine Learning Model Development and Deployment: Design, develop, and implement machine learning models, including generative AI models, to enhance our platform's capabilities.

Collaborative Innovation: Work closely with product managers, engineers, and designers to integrate machine learning solutions into consumer-facing products.

Model Optimization for Production Environments: Continuously refine models for improved performance, scalability, and cost-effectiveness.

Data Visualization: Create clear and compelling visualizations to communicate findings to stakeholders.

Stay Current: Keep abreast of the latest developments in AI and data science to ensure our technologies remain cutting-edge.

Ownership & Accountability: Take responsibility for the quality and timely delivery of data projects, acting as an owner throughout the process.