Senior Machine Learning Engineer - Credit

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

Senior Machine Learning Engineer at Plaid focused on credit products. The role involves designing, building, and deploying scalable ML solutions and systems, experimenting with new modeling techniques, and owning the full model lifecycle from training to serving and monitoring. Collaboration with cross-functional teams to define the ML roadmap is also a key aspect.

What you'd actually do

  1. Build machine learning systems that empower millions of users through well-known and emerging fintech applications with access to financial services
  2. Experiment with cutting-edge machine learning modeling techniques across high-impact credit use cases
  3. Work on both 0-1 stage problems and scaling systems from 1-10
  4. Develop AI and machine learning models across the full lifecycle, from offline training to online serving and monitoring
  5. Design, build, and deploy scalable ML solutions and systems in a production environment

Skills

Required

  • Python
  • Spark
  • Jupyter notebooks
  • standard machine learning libraries
  • experience training and serving AI and machine learning models in a production environment
  • experience in fintech lending
  • experience building or working with data-intensive backend applications in large distributed systems
  • strong ownership mindset
  • track record of driving projects to business impact
  • ability to work effectively with both technical and non-technical teams
  • Master’s degree or equivalent work experience in Computer Science, Mathematics, Engineering, or a closely related field

Nice to have

  • data analytics experience
  • data engineering experience

What the JD emphasized

  • training and serving AI and machine learning models in a production environment
  • fintech lending
  • strong understanding of how models are built in that space
  • Ability to code and iterate independently

Other signals

  • designing, building, and deploying scalable ML solutions and systems
  • lead experimentation with new modeling approaches and strategies
  • productionizing models
  • build the next wave of cash flow based underwriting
  • own AI and machine learning work across the full model lifecycle, from offline training to online serving and monitoring
  • help define the ML roadmap