Senior Machine Learning Engineer (Risk) at Earnin
Mountain View, California, USA -
Full Time


Start Date

Immediate

Expiry Date

05 Dec, 25

Salary

283800.0

Posted On

06 Sep, 25

Experience

4 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Financial Services

Description

ABOUT EARNIN

As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks.
We’re fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. We’re growing fast and are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey.

POSITION SUMMARY

Machine learning is integral to every financial service we provide. As we embark on a transformative phase, EarnIn is making significant investments to innovate and set new standards in ML applications within fintech. This role will focus on developing groundbreaking solutions through financial risk modeling, generating substantial business and social impact.
The base salary range for this full-time position is $232,200 - $283,800, plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position requiring 2 days a week in our Mountain View office

Responsibilities
  • Design, develop, A/B test, and deploy risk models while collaborating with data scientists to drive data-driven decisions.
  • Enhance credit and fraud models by incorporating innovative features every quarter and leveraging the latest industry research.
  • Monitor feature and model health, and communicate changes in model decisions.
  • Explore and integrate advanced technologies, including deep learning and LLMs, in the risk domain.
  • Lead by example to foster operational excellence and transformative change.
  • Expand responsibilities as new products emerge.
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