About the job
About EarnIn
EarnIn is a trailblazer in the earned wage access space, dedicated to empowering individuals with financial flexibility tailored for those living paycheck to paycheck. Our community members can access their earnings in real time, enabling them to spend, save, and enhance their financial well-being without burdensome fees, interest rates, or credit checks.
With a robust leadership team and esteemed funding partners such as A16Z, Matrix Partners, DST, and Ribbit Capital, we are positioned for rapid growth and are eager to attract top-notch talent to shape our future.
Position Summary
At EarnIn, machine learning (ML) is pivotal to our growth strategy. We harness advanced ML models to inform business decisions and enhance customer experiences. Your role will involve developing and implementing cutting-edge ML systems that leverage our data capabilities. You'll be tasked with thorough formulations, rigorous experimentation, and transforming ML models into efficient production-ready code, while also focusing on robustness monitoring and system logging/alarming.
We are inviting enthusiastic students and recent graduates with a solid foundation in machine learning, deep learning, language models, generative AI, programming, and data analysis to join our 12-week Machine Learning Internship Program. You will engage in real-world projects, collaborate with industry experts, gain invaluable insights into the fintech sector, and contribute to meaningful business and social outcomes. This position requires hybrid work from our Mountain View office, with 2 days per week on-site. Compensation for this internship is set at $40 per hour, with a commitment of 40 hours per week for the duration of the program.
What You'll Do
- Train and optimize large-scale Foundation Models for diverse fintech applications.
- Work with extensive datasets, encompassing both structured and unstructured data.
- Contribute to enhancements of our existing ML systems through model, data, or experimental upgrades.
- Acquire hands-on experience with a variety of technologies, including PyTorch, AWS, Kafka, Databricks, and more.
