Machine Learning Engineer - Embedded Insights

Plaid Plaid · Fintech · San Francisco, CA · All Departments

Machine Learning Engineer focused on building and deploying AI/ML models into customer-facing financial products, driving initiatives from concept to production, and ensuring model performance and scalability.

What you'd actually do

  1. Opportunity to shape Plaid’s future as a company where intelligence products are a core value proposition.
  2. Dive into one of the most unique datasets available in the industry and shape the strategy to leverage its value.
  3. Work across many different areas and learn deeply about the entire Plaid product suite
  4. Build products that empower millions of people to achieve financial freedom and opportunity.
  5. Work closely with customers to ensure products meet their needs and demonstrate true impact.

Skills

Required

  • 5+ years of experience in machine learning
  • deploying machine learning models into real-world, customer facing systems
  • High agency and creativity
  • experience identifying, defining, and proposing high impact machine learning opportunities
  • Ability to analyze large and complex financial datasets to derive insights
  • Proficiency in SQL
  • Proficiency in Python
  • Proficiency in data visualization/analysis tool
  • Ability to clearly communicate complex technical systems and decision making

Nice to have

  • Advanced degree or equivalent work experience in Statistics, Economics, Mathematics, Data Science, or a related field.

What the JD emphasized

  • deploying machine learning models into real-world, customer facing systems
  • High agency and creativity
  • identifying, defining, and proposing high impact machine learning opportunities

Other signals

  • drive machine learning initiatives from concept to production
  • leverage Plaid’s unique datasets to identify high-impact opportunities for machine learning
  • develop proofs of concept to validate new approaches
  • build MVP solutions that demonstrate customer value
  • translate successful prototypes into scalable, customer-facing products
  • optimizing models for new use cases
  • improving system scalability
  • maintaining and enhancing existing machine learning systems
  • robust monitoring frameworks
  • ensure model performance, reliability, and long-term health