Block
Senior Machine Learning Engineer, Model Risk Management
Remote 11003 Risk - Prod Dev - Square role with clear candidate location fit.
PostedRecently added
Eligible countries27 accepted countries
Seniority signalSenior
Work settingRemote
Accepted candidate locations
Role overview
Senior Machine Learning Engineer, Model Risk Management
Requirements and responsibilities
Readable role content extracted into sections for faster review.
You Will
- 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.
You Have
- 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.
Technologies We Use and Teach
- Python (NumPy, Pandas, scikit-learn, LightGBM, XGBoost, PyTorch)
- AI dev tools: Claude Code, Cursor, Copilot; agent skills and frameworks for building LLM-powered tooling
- MLflow / Databricks; Prefect on GCP Vertex AI
- Snowflake and cloud object storage
- GitHub and CI (ruff, pytest)
- Jira and Linear
- GCP and AWS
Similar roles
Keep a backup shortlist.
Stack
Use these tags to compare similar remote roles.
Location eligibility
Candidates should apply only when their profile country is listed here.
Your profileCountry not setSign in to check your country against this role.
Hiring flow
WithMira shows the role, then sends candidates to the company application.
1Check role fit, stack, and location eligibility in WithMira.
2Open the company application page from the tracked apply link.
3Save the role or subscribe for similar opportunities before leaving.