Founding ML Engineer, Revenue+ and Subscription Growth

Snap; New York (NY); On-site; USD 209,000 - 313,000; Full time

Benefits offered by this job

paid parental leave

comprehensive medical coverage

emotional and mental health support

Job summary

Snap Inc. is seeking a founding Machine Learning Engineer for the Revenue+ team to establish ML as a core capability for Snapchat+ monetization.

You will tackle personalization, offer decisioning, retention, and lifecycle optimization at large scale, collaborating with product and engineering to drive revenue growth. The role requires strong ML foundations, product sense, and ownership of end-to-end ML initiatives, with equity in RSUs and a pay structure reflecting multiple zones in the U.S.

Qualifications

• Bachelor's degree in a technical field or equivalent practical experience.
• 5+ years of post-Bachelor’s machine learning experience or equivalent combinations (MS/PhD).
• Experience deploying ML systems for ranking, recommendation, personalization, or monetization.
• Ability to take ML problems from ambiguous statements to production solutions.

Responsibilities

• Identify high-value ML opportunities to drive subscription growth, monetization, and retention.
• Design, build, and deploy ML systems for personalization, ranking, and lifecycle optimization.
• Own end-to-end work from problem framing to production deployment and measurement.
• Set technical direction for a new Revenue+ ML capability and guide best practices.
• Collaborate with Product, Data Science, Backend, and Mobile Engineering to shape ML investments.
• Build reliable, observable ML systems that scale to Snapchat’s user base.

Skills

ML foundations

Product judgment

ML systems

Autonomy

Collaboration

Education

Bachelor's degree in CS or related field

Job description

Snap Inc. is seeking a founding Machine Learning Engineer for the Revenue+ team to establish ML as a core capability for Snapchat+ monetization.

You will tackle personalization, offer decisioning, retention, and lifecycle optimization at large scale, collaborating with product and engineering to drive revenue growth. The role requires strong ML foundations, product sense, and ownership of end-to-end ML initiatives, with equity in RSUs and a pay structure reflecting multiple zones in the U.S.