Senior Machine Learning Engineer - Embedded Insights

Plaid · Fintech · San Francisco, CA · All Cost Centers

Senior Machine Learning Engineer focused on building and deploying ML models into Plaid's financial products, working across the full ML lifecycle from experimentation to production scaling and monitoring. The role involves analyzing financial datasets, developing proof-of-concept solutions, and collaborating with product and engineering teams to integrate models and ensure their long-term performance and reliability.

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
  • identifying, defining, and proposing high impact machine learning opportunities
  • analyze large and complex financial datasets
  • SQL
  • Python
  • data visualization/analysis tool
  • 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; experience identifying, defining, and proposing high impact machine learning opportunities

Other signals

  • deploying and scaling production-ready models
  • integrate models into customer-facing products
  • optimize models for specific use cases
  • maintaining and improving model health
  • developing new features
  • determining effective retraining strategies
  • building monitoring frameworks