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Block, Inc.

Senior Machine Learning Engineer, Model Risk Management

RemoteUnited States only
Published
Role
AI / ML
Experience
Senior
$228.7k–$343.1k/yr
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Open to US only. Set where you work from to check your eligibility.

No BS summary

Senior ML Engineer for Model Risk Management. Requires quantitative degree/experience, deep applied ML/stats, and Python/SQL for production code. Must be fluent with modern AI (LLMs, agentic tools) and have experience in high-stakes domains like credit or fraud. Strong communication and independence needed to challenge model owners and defend conclusions.

Core skills

Model Risk ManagementMachine LearningLLMs

Required skills

PythonSQLNumPyPandasscikit-learnLightGBMXGBoostPyTorchClaude CodeCursorCopilotMLflowDatabricksPrefectVertex AISnowflakeGitHubpytestJiraLinear

Optional skills

model risk management frameworksfair-lending standards

Required languages

English

What you'll do

  • Independently challenge model owners across lending, fraud, and AML: reproduce their results, set and defend the acceptance thresholds, and own the call on whether a model is sound.
  • Hunt the silent errors that make metrics lie, and prove them out before they reach production.
  • Choose evaluation that holds up under real conditions: rare events, shifting populations, and drift that only shows up after launch.
  • Work hands-on in codebases you did not write, learning the data, configs, and conventions, and ship production code in the tooling you build to validate them.
  • Build the agentic validation tooling the team depends on, orchestrating agents that run in parallel.
  • Reason about ML systems end to end — how features, training, serving, monitoring, and scale fit together — to evaluate and challenge an owner's design.
  • Tie explainability and fair-lending findings on consumer credit models back to the model and product decisions that follow.
  • Help define how Block validates the systems at the frontier of production AI, setting standards where none exist yet.

What they require

  • 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.
  • Familiarity with model risk management frameworks and fair-lending standards, with the specifics learnable on the job.
  • The communication to explain and defend your conclusions to model owners and senior stakeholders, and the independence to operate under ambiguity.

Benefits

  • Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering.
🇺🇸 United StatesTechnologyEnterprise
$228.7k–$343.1k/yr