Machine Learning Intern (Applied ML)

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 the enabler for every financial service EarnIn provides its community members. We are going through transformative investments in machine learning platforms and algorithms. We aim to lead the innovation and operational excellence in machine learning for the fintech industry. We seek talented and motivated students and recent graduates with a strong background in machine learning, deep learning, language models and generative AI, programming, and data analysis to join our 12-week Machine Learning Internship Program. You will work on real-world projects, collaborate with experienced professionals, gain valuable experience in the fintech industry, and realize business and social impact. This role requires hybrid work from our Mountain View office, with 3 days a week in person. This internship will pay $40 an hour with an expected 40 hours of work per week for the 12-week program. 

WHAT YOU'LL DO

• Collaborate with the machine learning team to design, develop, and implement machine learning models and algorithms.
• Understand the key metrics needed for such model evaluations.
• Analyze large and complex datasets to identify patterns, trends, and insights that can drive business decisions.
• Develop and maintain codebases for machine learning projects, ensuring best practices in coding and documentation.
• Stay current with the latest developments in machine learning, deep learning, and fintech, and share your knowledge with the team.
• Gain hands-on experience with various technologies, including Pytorch, AWS, Kafka, Databricks, etc.

WHAT WE’RE LOOKING FOR

• Currently pursuing or recently completed a Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Electrical Engineering, Statistics, or a related field.
• Strong foundation in machine learning, deep learning, and statistical analysis.
• Strong programming skills in Python, and familiarity with ML frameworks such as TensorFlow or PyTorch.
• Experience with data manipulation and analysis using tools like Pandas, NumPy, or SQL.
• Excellent problem-solving skills and the ability to work independently and collaboratively in a team environment.
• Strong communication skills, both written and verbal.

At EarnIn, we believe that the best way to build a financial system that works for everyday people is by hiring a team that represents our diverse community. Our team is diverse not only in background and experience but also in perspective. We celebrate our diversity and strive to create a culture of belonging. EarnIn does not unlawfully discriminate based on race, color, religion, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, registered domestic partner status, sexual orientation, genetic information, or any other basis protected by local, state, or federal laws. EarnIn is an E-Verify participant. 

EarnIn does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or HR team.