About the role
At Playlist, life's richest moments happen when people step away from screens to move, connect, explore, and play. We're building the definitive platform for intentional living, connecting people with inspiring experiences in fitness, wellness, and beyond. With popular brands like Mindbody and ClassPass, Playlist empowers businesses and individuals, making it effortless for aspirations to become actions. Join us in reshaping technology's role to foster meaningful, real-world connections.
ClassPass offers thousands of fitness and wellness experiences worldwide, helping people lead active, balanced lifestyles. Our platform makes discovering and enjoying activities simple, personalized, and joyful—whether it's fitness classes, self-care sessions, a healthy lunch, or a new adventure. Join us in shaping healthier, more vibrant communities around the globe.
Who we are
We're a passionate engineering, platform, and operations team, developing and supporting ground-breaking products. Together, we'll build for the future, creating more opportunities for wellness businesses around the world to help their customers lead full, healthy lives. We push ourselves and our company to always strive for this higher purpose, recognizing the power in working together toward the same goal. We believe in building a diverse company where everyone feels safe bringing their authentic selves to work. And we believe that the secret to success is our people. Join the team, and let's see what we can accomplish together!
About the team
The ClassPass ML & Data team builds the models and data systems that power core parts of the member and partner experience, including pricing, personalization, search, and inventory. We work closely with data scientists, product managers, and engineering teams across the company, and we value engineers who bring their own ideas and see them through to measurable impact. We're growing the team and looking for an engineer who loves turning data into real business results.
You'll likely spend time working on
- Owning features end to end: discovery, analysis, defining success metrics, building, launching, and measuring impact
- Designing, building, and modernizing data models and ETL pipelines in dbt and Snowflake
- Building, deploying, and monitoring ML models in production for problems like recommendations, personalization, pricing, and marketplace density
- Testing hypotheses quickly, with data and statistical rigor
- Writing technical design documents (TDDs) and partnering with engineers, data scientists, and product managers to shape solutions