Inactive
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EarnIn

On-demand wage access via app

Machine Learning Intern

Applied Machine Learning +1Posted on Apr 8
Internship · Summer 2026Mountain View, CA, USA$40 /hr

About the Job

Requirements

  • Actively pursuing a Master's or PhD in Computer Science, Information Technology, or a related field.
  • Located in Mountain View, or have the ability to relocate there, for the duration of the internship.
  • Strong understanding of statistical models, familiarity, and in-depth understanding of machine learning and deep learning algorithms.
  • Familiarity with training or fine-tuning large-scale models, Sequence Transformer models.
  • Interest in multimodal or multitask learning across structured, sequential, and behavioral data.
  • Familiarity with AI tools, harness engineering, agentic workflow, etc.
  • Hands-on programming experience in Python and ML frameworks such as PyTorch.
  • Equipped with good verbal and written communication skills.
  • A background demonstrating strong problem-solving skills.
  • Committed to taking ownership of projects, conducting thorough investigations, and driving initiatives to conclusion.

Responsibilities

  • Train and fine-tune large-scale Foundation Models to support various fintech product use cases
  • Work with a large dataset, including structured and unstructured data
  • Help in ensuring improvements in our current ML systems via model, data, or experimentation upgrades
  • Gain hands-on experience with a wide array of technologies, including PyTorch, AWS, Kafka, Databricks, etc

About the Company

EarnIn

On-demand wage access via app

EarnIn provides wage access through a mobile app, allowing users to cash out a portion of their earned wages before payday without traditional fees or interest. Users choose how much to withdraw, with the deduction taken from their next paycheck; Balance Shield automates savings by topping up if balance falls too low; Tip Jar encourages saving and community contributions. The model relies on voluntary payments from users, enabling pay-what-you-want contributions rather than mandatory fees. The company emphasizes a community-driven approach to financial fairness and security with 256-bit encryption.

Company size
501-1,000
Company stage
Debt Financing
Total funding
$565.1M
Headquarters
Mountain View, CA
Founded
2012

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