ABOUT ROCKET MONEY
Rocket Money's mission is to empower people to live their best financial lives. Rocket Money offers members a unique understanding of their finances and a suite of valuable services that save them time and money – ultimately giving them a leg up on their financial journey.
ABOUT THE TEAMMachine Learning Engineers at Rocket Money further our mission by building products that deepen customer relationships with our many financial products. Our work ranges from transaction enrichment to personalization to cross-functional tools that support various AI product initiatives. We work closely with product teams to develop features that help customers understand, track, and improve their personal finances. We have a strong preference for team players that are comfortable collaborating across teams, know how to support strategy with ML and AI powered user experiences, can deliver scalable and high quality user experiences, and understand the effects of their products on end users. At the Staff level, Machine Learning Engineers are expected to build broad expertise into our products and the ML solutions that power them as well as drive technical progress for the team.
ABOUT THE ROLEAs a Staff Machine Learning Engineer at Rocket Money, you will be at the forefront of our ML and AI product development, bringing your expertise to design, implement, and maintain sophisticated ML systems that drive our product experiences. You will:
Potential Projects:
Additional information: Salary range of $210,000 - $260,000/year + bonus + benefits. Base pay offered may vary depending on job-related knowledge, skills, and experience.
Rocket Money is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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