Senior Staff Machine Learning Engineer, (ml Underwriting)

Affirm Affirm · Fintech · Canada, United States · Remote · Checkout

Senior Staff ML Engineer at Affirm focused on ML Underwriting. This role involves defining and driving multi-year technical strategy for ML, leading the design, implementation, and scaling of advanced ML systems, and providing broad technical leadership across the ML organization. The engineer will mentor senior engineers, shape long-term modeling capabilities, and ensure operational excellence for critical ML systems. Experience with large-scale, real-time ML systems, end-to-end ML system design, Python, PyTorch, XGBoost, and distributed ML infrastructure is required.

What you'd actually do

  1. You will define and drive multi-year, multi-team technical strategy for machine learning across Affirm, ensuring alignment with company-wide priorities and influencing the roadmaps of partner teams and platforms.
  2. You will lead the design, implementation, and scaling of advanced ML systems, setting the architectural direction for complex, cross-functional initiatives and ensuring systems remain reliable, extensible, and prepared for increasingly sophisticated modeling workloads.
  3. You will partner deeply with ML Platform, product, engineering, and risk leadership to shape long-term modeling capabilities, define new opportunities for ML impact, and guide infrastructure evolution required for next-generation ML methods.
  4. You will provide broad technical leadership across the ML organization, mentoring senior engineers, elevating design and code quality, and spreading ML expertise through documentation, talks, and cross-org guidance.
  5. You will drive clarity and alignment on ambiguous, high-stakes technical decisions, resolving cross-team tensions, balancing competing priorities, and exercising judgment optimized for the broader engineering organization.

Skills

Required

  • Python
  • PyTorch
  • XGBoost
  • Kubeflow
  • MLflow
  • Spark
  • Ray
  • representation learning
  • embedding-based modeling
  • neural network-based sequence modeling
  • Transformers
  • recurrent models
  • attention-based models
  • multi-task learning
  • streaming or batch data ingestion
  • feature stores
  • feature engineering
  • training pipelines
  • model serving and inference infrastructure
  • monitoring
  • automated retraining

Nice to have

  • PhD in a related field

What the JD emphasized

  • 10+ years of experience researching, designing, deploying, and operating large-scale, real-time machine learning systems
  • end-to-end ML system design
  • large-scale distributed ML infrastructure
  • strong technical leadership

Other signals

  • ML Underwriting
  • advanced modeling approaches
  • scale advanced ML systems
  • next-generation ML methods