Machine Learning Engineer, Snap Inc.

• Strong understanding of machine learning and software engineering foundations
• Strong product and business judgment, with the ability to identify where ML can create measurable incremental value
• Experience with ranking, recommendation, personalization, propensity modeling, decisioning, or related product ML systems
• Ability to independently turn ambiguous business problems into concrete ML opportunities and technical plans
• Ability to operate with substantial autonomy and take end-to-end technical ownership
• Strong collaboration and mentorship skills
• 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
• 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 years of post-grad machine learning experience
• 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

Desirable

• We’re looking for a founding Machine Learning Engineer to join the Revenue+ team and help establish machine learning as a core capability for subscription growth and monetization across Snapchat+ subscription
• You’ll work on problems such as personalized paywalls, offer decisioning, retention, lifecycle optimization, and subscriber value, with direct and measurable impact on revenue
• Identify 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 full path from ambiguous business problem and data exploration through experimentation, production deployment, measurement, and iteration
• Establish technical direction and best practices for a new Revenue+ ML capability
• Partner closely with Product, Data Science, Backend, and Mobile Engineering to shape product strategy and prioritize ML investments
• Build reliable, observable, scalable production ML systems serving Snapchatters at significant scale
• Utilize AI tools to design and ship scalable services while upholding rigorous standards for code correctness, security, and production