Revenue+ ML Engineer - Personalization & Monetization

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

Benefits offered by this job

Equity in RSUs

Medical coverage

Parental leave

Job summary

Snap Inc. is seeking a founding Machine Learning Engineer to join the Revenue+ team to establish ML as a core capability for Snapchat+. You will work on personalization, offer decisioning, and lifecycle optimization at high scale.

You will partner with Product, Data Science, Backend, and Mobile Engineering to shape strategy, deploy ML systems, and ensure production-grade, privacy-conscious solutions.

Qualifications

• Bachelor's degree in a relevant technical field or equivalent experience.
• 5+ years of post-Bachelor ML experience or PhD + 1 year.
• Experience productionizing ML systems for ranking, monetization, retention, or related product ML applications.
• Ability to turn ambiguous business problems into concrete ML opportunities and technical plans.

Responsibilities

• Identify high-value ML opportunities for subscription growth.
• Design, build, deploy ML systems for personalization, ranking, propensity modeling, offer decisioning, lifecycle optimization.
• Own the end-to-end lifecycle from problem definition to production deployment and measurement.
• Establish technical direction for the Revenue+ ML capability.
• Collaborate with Product, Data Science, Backend, and Mobile Engineering to shape ML investments.
• Build reliable, scalable production ML systems for Snapchatters at scale.

Skills

ML fundamentals

Product judgment

Autonomy

Collaboration

Security & privacy

Education

Bachelor's degree in CS or related field

Advanced degree (optional)

Tools

AI tools

Job description

Snap Inc. is seeking a founding Machine Learning Engineer to join the Revenue+ team to establish ML as a core capability for Snapchat+. You will work on personalization, offer decisioning, and lifecycle optimization at high scale.

You will partner with Product, Data Science, Backend, and Mobile Engineering to shape strategy, deploy ML systems, and ensure production-grade, privacy-conscious solutions.