Senior Machine Learning Engineer, Model Risk Management

A quantitative degree or equivalent experience, and senior-IC depth building or validating models in a high-stakes domain such as credit, fraud, or financial crime.

Command of effective-challenge methodology: reproduction, conceptual-soundness review, benchmarking, stress testing, and outcomes analysis, with an eye for how a model holds up after launch and where its assumptions break.

Deep applied ML and statistics across model families, from regression and tree ensembles to deep learning, with sound judgment about evaluation, calibration, and generalization.

Experimentation and statistical rigor: holdout and experiment design, reasoning about uncertainty, and evaluating a model beyond aggregate accuracy.

Solid software and data engineering: production-quality Python, SQL on large datasets, and reproducible, tested code.

Fluency with modern AI: building with LLMs and agentic tools, and the judgment to know when their output can be trusted.

Block lends, moves money, and screens for financial crime at enormous scale, and one bad model can mean millions in credit losses, suspicious activity that goes unreported, or a fair lending violation.

Model Risk Management is the independent function that decides whether a model is sound enough to put in front of customers and regulators.

The failures that matter rarely announce themselves: a model can clear every headline metric and still be broken underneath.

It can pass clean at launch and then quietly drift as the population shifts, until the loss it was supposed to prevent surfaces months later.

The hard part is finding what looks right and is wrong, then proving it well enough to hold up under questioning.

Much of the work arrives under-specified, so you scope it into a defensible plan, ask the questions that surface the real requirements, and defend your tradeoffs to the people who built the model you are challenging.