Machine Learning Engineer II

CLEAR is building THE secure identity company of the future. Our mission is to make experiences safer and easier — physically and digitally. With more than 38 million members and a growing network of partners across the world, CLEAR's secure identity platform is transforming the way people live, work, and travel. Whether it's at the airport, stadium, or throughout your everyday life, CLEAR unlocks the magic of frictionless experiences.

We're looking for an experienced Machine Learning Engineer to help us build the next generation of products which will go beyond just ID and enable our members to leverage the power of a networked digital identity. As a Machine Learning Engineer at CLEAR, you will participate in the design, implementation, testing, and deployment of applications to build and enhance our platform — one that interconnects dozens of attributes and qualifications while keeping member privacy and security at the core.

A brief highlight of our tech stack: Python / Postgres / Snowflake / dbt AWS SageMaker and MLflow

How You'll be Rewarded At CLEAR we help YOU move forward. You'll work with talented team members who are motivated by our mission of making experiences safer and easier. In our offices, you'll enjoy benefits like meals and snacks. We invest in your well-being and learning & development with our stipend and reimbursement programs. We offer comprehensive healthcare plans, family building benefits, flexible time off, free OneMedical memberships for you and your dependents, and a 401(k) retirement plan with employer match.

The total compensation range for this role is $180,000 - $220,000, depending on levels of skills and experience. This range represents the combined base salary and new hire equity package (in Restricted Stock Units). This role is also eligible for refresh equity grants.

What you're great at 3+ years of experience building, operating, and scaling ML models for consumer applications, particularly building end-to-end systems. Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance. Expertise in best practices for feature engineering, model development/deployment, and monitoring. Articulating technical concepts to a mixed audience of technical and non-technical stakeholders. Collaborating with and mentoring less experienced members of the team. Comfort with ambiguity. Curiosity about technology, belief in constant learning, and the ability to work autonomously to figure out what's important.