Job description

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company operates Snapchat , a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc. , a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji , Saturn, and other digital services. Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront. 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. What you’ll do: 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 Knowledge, Skills & Abilities: 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 Minimum Qualifications: 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 Preferred Qualifications: Advanced degree in computer science or related field Experience with subscription, monetization, pricing, offers, retention, or lifecycle optimization Experience of extensive collaboration with Backend and Mobile SWEs Experience with causal inference, uplift modeling, experimentation, customer lifetime value, or incremental impact measurement 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 Default Together Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4 days per week. At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable). Our Benefits : Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success! Compensation In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future. Zone A (CA, WA, NYC) : The base salary range for this position is $209,000-$313,000 annually. Zone B : The base salary range for this position is $199,000-$297,000 annually. Zone C : The base salary range for this position is $178,000-$266,000 annually. This position is eligible for equity in the form of RSUs. If you believe this job description is missing required pay transparency information, please submit a report through this form : Job Description Pay Range Disclosure .

Requirements

Must-haves

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
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
Bachelor s Degree in a relevant technical field such as computer science or equivalent years of practical work 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

Nice-to-haves

Advanced degree in computer science or related field
Experience with subscription, monetization, pricing, offers, retention, or lifecycle optimization
Experience of extensive collaboration with Backend and Mobile SWEs
Experience with causal inference, uplift modeling, experimentation, customer lifetime value, or incremental impact measurement
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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlai

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Parental Leave