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Machine Learning Engineer

Roles & Responsibilities

  • 3+ years of experience designing and deploying ML models in a high-scale production environment.
  • Strong knowledge of machine learning algorithms and data analysis techniques.
  • Strong proficiency in programming languages like Python, R, or similar, and experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
  • Enthusiastic about building ML infrastructure from scratch in a nascent environment.

Requirements:

  • Develop and implement machine learning models to evaluate disputes and chargebacks and likelihood of fraud.
  • Create and manage an orchestration layer for multiple models, enabling automated and supervised decision-making processes.
  • Collaborate with stakeholders to leverage valuable data from partners and customers, ensuring a continuously learning experience and first-class user experience.

Job description

About Casap

Casap is a Series A startup that has raised over $30M from Emergence, Lightspeed, and Primary Ventures. Based in San Francisco, the company was founded by product leaders from Robinhood and Chime. We are on a mission to change the way banks operate by automating disputes and fighting friendly fraud. People love what we’ve built, from everyday users to the biggest names in finance.

Casap is looking for a talented Machine Learning Engineer in San Francisco excited to supercharge how banks operate with a rapidly scaling product at the forefront of automation and Artificial Intelligence.

Responsibilities

  • Develop and implement machine learning models to evaluate disputes and chargebacks and likelihood of fraud

  • Create and manage an orchestration layer for multiple models, enabling automated and supervised decision-making processes

  • Collaborate with stakeholders to leverage valuable data from partners and customers, ensuring a continuously learning experience and first-class user experience

Qualifications

  • 3+ years of experience designing and deploying ML models in a high-scale production environment.

  • Strong knowledge of machine learning algorithms and data analysis techniques.

  • Enthusiastic about building ML infrastructure from scratch in a nascent environment.

  • Ability to think like a product engineer, balancing technical innovation with practical application and user needs.

  • Strong proficiency in programming languages like Python, R, or similar, and experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.

  • Excellent problem-solving skills and the ability to work independently and in a collaborative team environment.

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