Snap
Machine Learning Engineer, Revenue+, Level 5
$178K–$313K
New York, New York · Full-time · Posted Oct 6, 2026
- Experience
- 5+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 4+ years of post-grad machine learning experience; or PhD in a relevant technical field + 1 year of post-grad machine learning experience.
- Education
- Bachelor’s degree in a relevant technical field such as computer science, or equivalent years of practical work experience.
About the job
Build and lead production machine-learning capabilities for Snapchat+ subscription growth, monetization, retention, and subscriber value.
What you'll do
- Identify subscription opportunities for ML and own work from problem discovery through experimentation, deployment, measurement, and iteration.
- Design, deploy, and improve scalable ML systems for personalization, ranking, propensity modeling, offer decisioning, and lifecycle optimization.
- Set technical direction and collaborate with Product, Data Science, Backend, and Mobile Engineering.
What they ask for
- Bachelor’s degree in a relevant technical field or equivalent years of practical work experience.
- Experience developing and productionizing ML systems for relevant product applications, and taking them from ambiguous problems through experimentation into production.
- Strong ML and software engineering foundations, product and business judgment, autonomy, and end-to-end technical ownership.
Nice to have
- Advanced degree in computer science or a related field.
- Experience in subscription monetization or retention, causal inference or uplift modeling, large-scale production ML, or founding/early ML roles.
Pay
$209,000-$313,000 annually (Zone A; base salary); $199,000-$297,000 annually (Zone B; base salary); $178,000-$266,000 annually (Zone C; base salary)
Tools
machine learning, ranking, recommendation, personalization, propensity modeling, decisioning, AI tools
AI focus
Build ML systems for subscription personalization, monetization, retention, and lifecycle optimization; use AI tools to design and ship scalable services, auditing generated output for correctness, performance, and security.
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