Senior Applied Machine Learning Engineer - Core ML(Growth)

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. Since our founding, our app has been downloaded over 13M times and we have provided access to $10 billion in earnings.

We’re fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, 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.

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Position Summary:

As a Fintech company where Machine Learning (ML) is one of the key features, our operations highly rely on machine learning models, from business decisions to customer experiences. Therefore, ensuring our machine learning systems’ health and scalability is critical. 

To guarantee the success of machine learning systems, work around transforming ML models to high-performance production level code needs to be done, including not only implementing sophisticated machine learning algorithms but also robustness monitoring, system logging/alarming, and devOps.

This is a remote position. The US base salary range for this full-time position is $140,000 - $288,000 + equity + benefits. Our salary ranges are determined by role, level, and location.

What You'll Do:

• Design, build, and launch efficient and reliable machine learning (ML) services
• Build and maintain interfaces where the model predictions and decisions are served 
• Improve logging, monitoring, alarming of the ML services
• Track business performance of models with ad-hoc reporting
• Build and own an automated rolling-out mechanism for ML models

What We're Looking For: