This role pays less than
70%
of similar roles. Most pay
$186,987–$254,750
— the shaded band above.
At the midpoint, this role pays about
$195k
versus about
$221k
for comparable roles.
Based on 240 similar postings.
Employer
About Affirm
Affirm is a buy-now, pay-later (BNPL) financial technology company that offers point-of-sale installment loans to consumers, allowing them to split purchases into fixed monthly payments with transparent terms. Industry: Financial Technology & Consumer Lending
Affirm currently has
60 open roles
on FindRole.
Listed pay typically runs
$195,000–$255,000
across 60 roles with salary data.
Machine Learning Engineer II (Underwriting ML) joins the Underwriting ML team to build and improve machine learning systems that make real-time transaction decisions by assessing repayment risk and expected value for every checkout. The role involves developing prediction models for tabular and sequential data, building scalable feature pipelines from proprietary and third-party signals, and prototyping new modeling ideas through offline experiments. Responsibilities include productionizing models into batch or real-time systems, ensuring reliability and low latency, and monitoring model health via retraining workflows. Candidates must possess strong Python skills, experience with gradient-boosted decision trees like LightGBM, XGBoost, or CatBoost, and proficiency in deep learning frameworks like PyTorch. Additional required expertise includes distributed data processing using Spark, Ray, or Dask, as well as ML lifecycle tooling such as Kubeflow, Airflow, or MLflow to manage training and experimentation.
What does a Machine Learning Engineer earn in Remote?
Median $229700 from 72 postings across 27 companies.
Develop and iterate on underwriting prediction models for tabular and sequential data.
Build and scale feature pipelines and training datasets from proprietary and third-party signals.
Prototype new modeling ideas and run offline experiments to identify high-performing approaches.
Integrate models into batch and real-time decision systems while improving reliability and latency.
Instrument and monitor model and data health to define retraining and backtesting workflows.
Write production-quality Python code for complex software components and large codebases.
Communicate technical results and trade-offs clearly to both technical and non-technical stakeholders.
What we're looking for
Candidates must have at least 2 years of experience as a machine learning engineer or a PhD in a relevant field.
A Bachelor's degree in a related field is required if the candidate does not have equivalent practical experience.
Strong Python skills and experience writing production-quality code are required.
Experience building and evaluating models for classification problems, preferably using gradient-boosted decision trees like LightGBM, XGBoost, or CatBoost.
Experience with a deep learning framework, preferably PyTorch.
Experience with distributed data processing or parallel compute frameworks, such as Spark, Ray, or Dask.
Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring, such as Kubeflow, Airflow, or MLflow.
Proficiency in using AI-powered developer tools to accelerate iteration, debugging, and code quality.
Machine Learning
Python
Golang
TensorFlow
PyTorch
Deep Learning
Recommendation Systems
Model Training
Production Systems
Regression Analysis
Hypothesis Testing
Data Science