# Machine Learning Engineer II (Underwriting ML)

**Company**: Affirm
**Location**: Remote Canada
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Salary**: $133,000 - $183,000
**Category**: Engineering
**Industry**: Finance

**Apply**: https://job-boards.greenhouse.io/affirm/jobs/7822389003?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_e0eaa148-dbf

## Description

At Affirm, we exist for the moments that matter,giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most.

On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout.

Responsibilities:

- Develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data

- Build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed

- Prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls

- Help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness

- Instrument and monitor model and data health, and help define retraining/backtesting workflows

- Collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences

Requirements:

- 2+ years of experience as a machine learning engineer or a PhD in a relevant field

- Strong Python skills and experience writing production-quality code

- Experience building and evaluating models for classification problems

- Experience with a deep learning framework (PyTorch preferred)

- Experience working with distributed data processing or parallel compute frameworks (Spark preferred)

- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring

- Proficient in using AI-powered developer tools to accelerate iteration, debugging, and code quality

- Mastered taking a simple problem or business scenario into a solution that interacts with multiple software components

- Comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews

- Strong verbal and written communication skills

Benefits:

- Health coverage at no cost: 100% of premiums for employees and their dependents

- Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options

- Time off to recharge: Flexible time off and generous holiday calendars

- Own a piece of what you build: Employee stock purchase plan (ESPP)

## Skills

### Required
- Python
- machine learning
- deep learning
- PyTorch
- Spark
- ML lifecycle tooling

### Nice to have
- LightGBM
- XGBoost
- CatBoost
- Ray
- Dask
- Kubeflow
- Airflow
- MLflow

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/affirm/jobs/7822389003?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)