Machine Learning Engineer - PyTorch & Spark

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 the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing repayment risk and expected value for every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from initial concept to prototype to production, ensuring they stay healthy through robust measurement and monitoring as user behavior and macroeconomic conditions evolve.

Key Responsibilities

• Model Development & Iteration: Develop and iterate on underwriting prediction models using various machine learning techniques tailored for tabular and sequential data.
• Data & Feature Engineering: Build and scale feature pipelines and training datasets from proprietary and third-party signals, collaborating with data and platform engineering teams.
• Prototyping & Experimentation: Prototype new modeling concepts, execute offline experiments, and drive top-performing algorithms into production under strict risk controls.
• Productionization: Integrate machine learning models into real-time and batch decision engines while optimizing system reliability, latency, and operational performance.
• Model Health & Observability: Instrument monitoring systems for data and model health, contributing to backtesting and automated retraining workflows.
• Cross-Functional Collaboration: Partner with Engineering, Risk Analytics, Product, and ML Platform teams to define requirements, evaluate trade-offs, and communicate results to technical and non-technical partners.

Requirements

Experience & Education

• 2+ years of professional experience as a Machine Learning Engineer OR a PhD in a relevant quantitative field.
• Bachelor’s degree in a related field or equivalent practical experience.

Technical Skills

• Strong Python programming skills with a track record of writing clean, production-quality, and extensible code.
• Hands-on experience building and evaluating classification models (preferably gradient-boosted decision trees like LightGBM, XGBoost, CatBoost).
• Experience with deep learning frameworks (preferably PyTorch).
• Experience with distributed computing or parallel data processing frameworks (preferably Spark; Dask, Ray, or similar).
• Experience with ML lifecycle and orchestration tooling (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).
• Proficiency with AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality.

Soft Skills & Engineering Practice

• Proven ability to navigate large codebases, debug existing software, and conduct thorough code reviews.
• Strong proactive growth mindset, actively seeking feedback from managers, peers, and stakeholders.
• Clear written and verbal communication skills for effective global engineering collaboration.

Benefits & Workplace Perks

• 100% Health Coverage: 100% covered health, dental, and vision insurance premiums for employees and their dependents.
• Spending Stipends: Monthly stipends supporting home office technology setups, health, and wellness choices.
• Equity: Employee Stock Purchase Plan (ESPP) allowing employees to purchase Affirm stock at a discount.
• PTO & Flexibility: Flexible paid time off, generous holiday schedules, and remote-first work flexibility with an in-person onboarding experience.