Machine Learning Engineer (Revenue+)

Join Snap Inc. as a founding Machine Learning Engineer for the Revenue+ team. You will work on personalized paywalls, offer decisioning, retention, lifecycle optimization, and subscriber value, directly impacting revenue. You will design, build, and deploy ML systems, establish technical direction, and partner closely with various teams. The role offers a comprehensive benefits package, including generous time off, medical coverage, and a 401(k) plan.

• Identifying high-value opportunities where machine learning can improve subscription growth, monetization, retention, and subscriber value.
• Designing, building, and deploying ML systems for personalization, ranking, propensity modeling, offer decisioning, and lifecycle optimization.
• Establishing technical direction and best practices for a new Revenue+ ML capability, and partnering closely with Product, Data Science, Backend, and Mobile Engineering to shape product strategy.

• Experience with ranking, recommendation, personalization, propensity modeling, decisioning, or related product ML systems
• Strong understanding of machine learning and software engineering foundations
• Proficiency in, or a strong aptitude for, leveraging AI tools to streamline development, paired with the critical judgment to audit generated output for architectural integrity, performance bottlenecks, and security risks
• Ability to operate with substantial autonomy and take end-to-end technical ownership
• Ability to independently turn ambiguous business problems into concrete ML opportunities and technical plans
• Strong collaboration and mentorship skills
• Strong product and business judgment, with the ability to identify where ML can create measurable incremental value
• Experience developing and productionizing machine learning 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
• 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 years of post-grad machine learning experience
• Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience
• Advanced degree in computer science or related field
• Experience with subscription, monetization, pricing, offers, retention, or lifecycle optimization
• Experience with causal inference, uplift modeling, experimentation, customer lifetime value, or incremental impact measurement
• Experience of extensive collaboration with Backend and Mobile SWEs
• Experience optimizing ML systems against business or revenue outcomes
• Experience operating production ML systems at significant scale
• Experience as an early or founding ML engineer, or in another environment requiring broad technical and product ownership