EarnIn
Fintech
StaffMachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Staff Machine Learning Engineer at EarnIn. Skills: Machine Learning, MLOps, Software Engineering. Design, build, and launch machine learning models. Drive business impact”
Industry & Context.
Analytical skills
Potential travel to Palo Alto HQ
What They're Looking For.
Must Have
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, 5+ years of industry machine learning experience, Excellent software engineering skills, Programming skills in Python, Familiarity with ML frameworks such as TensorFlow, PyTorch, XGBoost and Scikit-Learn, Hands-on experience with SQL, Hands-on experience with data manipulation tools, Analytical skills to evaluate business impact and performance metrics of ML implementations, Communication skills, Collaboration skills
Nice to Have
Familiarity with digital marketing concepts and metrics, Prior experience with Marketing Mix Modeling (MMM), Prior experience with predicted Lifetime Value (LTV), Prior experience with incremental Lift estimation, Prior experience with geo lift tests, Prior experience with causal inference, Experience with ML cloud platforms such as AWS Sagemaker, Experience with ML cloud platforms such as Databricks, Experience with ML cloud platforms such as GCP Vertex AI
What You'll Do.
and launch machine learning models
Drive business impact
Find innovative solutions
Deliver high-quality code
Work with product managers
Work with machine learning engineers
Work with performance engineers
Work with quality engineers
Take the product to market
Demonstrate end-to-end understanding of applications
Develop deep understanding of models & systems
Partner with product managers
Partner with tech leads
Partner with stakeholders
Analyze business problems
Define scope of systems
Work closely with data platform teams
Enable robust scalable batch data pipelines
Enable real-time data pipelines
Drive high engineering standards
Drive engineering best practices
Implement automated testing
How You'll Work.
Team & Collaboration
Cross-functional teams; Product managers; Machine learning engineers; Performance engineers; Quality engineers; Data platform teams; Tech leads; Stakeholders
Communication Scope
Communication skills
Full Job 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. 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 over $15 billion in earnings. 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 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-performan
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