Data Science & Engineering Manager
Alltrails
·Aug 6, 2026
- Salary
- $170k – $215k/yr
- Location
- Remote
- Workplace
- Remote
- Type
- Full-time
- Department
- Engineering
- Seniority
- Manager
- Experience
- 5+ years
- Source
- Lever
Description
AllTrails is the app for exploring the outdoors. We help people spend more time outside by connecting them to their next adventure. Home to the world’s largest outdoor community, with members and trails around the globe, you can find AllTrails in the Apple App Store, Google Play Store, and at alltrails.com.
Every day, our team works to get more people outside and help our members have better experiences on the trail. We lead with our values–positivity, innovation, humility, adaptability, inclusivity, and stewardship–in everything we do. Join us!
This is a U.S. based remote position. While this role does not require in-person attendance, we have a strong preference for candidates based in or near one of these cities: San Francisco CA, Portland OR, Seattle WA, Denver CO, and New York NY. Being in one of these cities creates opportunities to connect with teammates through Trail Days, co-working sessions, and local gatherings while still having the flexibility to work remotely. San Francisco employees are highly encouraged to come to the office one day per week. Please note that, at this time, we’re unable to offer visa sponsorship for this role.
About the role
This role leads a small, centralized engineering function of AllTrails that cross-functionally supports the entire business cross-functionally across separate product, data analytics, finance, and marketing teams. The team sits within the Maps & Data org and reports to the Director of Engineering.
The team's scope spans ownership of scalable data infrastructure, data governance, reliability, compliance, and the development and productionization of machine learning, AI, and algorithmic solutions that power internal operations and member-facing product experiences. A core focus of this role is building and maturing the team's MLOps capabilities, including model deployment pipelines, monitoring, and lifecycle management, alongside a strong foundation in hands-on data pipeline design, development, and operation.
This manager will be both a technical leader and a hands-on practitioner: someone who can roll up their sleeves on pipeline architecture and code reviews while also driving the strategy and culture needed to ship data and ML systems reliably into production.