Machine Learning Engineer, Revenue+

Snap is building out machine learning capability across subscription growth and monetization for Snapchat+. As a founding Machine Learning Engineer for the Revenue+ team, you will help define how ML supports personalized paywalls, offer decisioning, retention, lifecycle optimization, and subscriber value, with ownership spanning problem framing through production and measurement. The role is based in New York, NY (onsite) and expects in-office presence 4+ days per week.

Role summary

You will identify where machine learning can drive meaningful, incremental improvements to subscription growth and monetization. You will then design, build, and deploy production-ready ML systems that enable personalization and decisioning across the subscriber lifecycle, while establishing a durable technical foundation for a new Revenue+ ML capability.

Responsibilities

• Find high-value opportunities where machine learning can improve subscription growth, monetization, retention, and subscriber value
• Design, build, and deploy ML systems for personalization, ranking, propensity modeling, offer decisioning, and lifecycle optimization
• Own the end-to-end process from ambiguous business problems and data exploration to experimentation, production deployment, measurement, and iteration
• Define technical direction and best practices for establishing a new Revenue+ ML capability
• Partner with Product, Data Science, Backend, and Mobile Engineering to shape product strategy and prioritize ML investment areas
• Build reliable, observable, scalable production ML systems serving Snapchatters at significant scale
• Use AI tools to accelerate development while maintaining rigorous standards for code correctness, security, and production reliability

Requirements

• Strong understanding of machine learning and software engineering foundations
• Strong product and business judgment, including the ability to pinpoint where ML can deliver measurable incremental value
• Experience with ranking, recommendation, personalization, propensity modeling, decisioning, or related product ML systems
• Ability to translate ambiguous business problems into concrete ML opportunities and technical plans
• Ability to work with substantial autonomy and take end-to-end technical ownership
• Strong collaboration and mentorship skills
• Proficiency in leveraging AI tools to streamline development, paired with judgment to audit generated output for architectural integrity, performance bottlenecks, and security risks
• Bachelor’s degree in a relevant technical field such as computer science, or equivalent years of practical work experience
• 5+ years of post-Bachelor’s machine learning experience, or Master’s degree in a technical field + 4+ year of post-grad machine learning experience, or PhD in a relevant technical field + 1 year of post-grad machine learning experience
• Experience developing and productionizing ML systems for ranking, recommendation, personalization, propensity modeling, decisioning, monetization, retention, or other relevant product ML applications
• Experience taking ML systems from ambiguous problem statements through experimentation and into production

Benefits

• Paid parental leave
• Comprehensive medical coverage
• Emotional and mental health support programs
• Compensation packages

Compensation

• Zone A (CA, WA, NYC): $209,000-$313,000 base salary range annually
• Zone B: $199,000-$297,000 base salary range annually
• Zone C: $178,000-$266,000 base salary range annually
• Equity eligibility in the form of RSUs