<div class="content-intro"><p>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.</p></div><div> </div><div><p>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. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve.</p><p> </p><p><strong>What you’ll do</strong></p><p>- You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data</p><p>- You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.</p><p>- You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.</p><p>- You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness.</p><p>- You will instrument and monitor model and data health, and help define retraining/backtesting workflows</p><p>- You will 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.</p><p> </p><p><strong>What we look for</strong></p><p>- You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field.</p><p>- Strong Python skills and experience writing production-quality code.</p><p>- Experience building and evaluating models for classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar).</p><p>- Experience with a deep learning framework (PyTorch preferred).</p><p>- Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar).</p><p>- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).</p><p>- Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.</p><p>- You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.</p><p>- You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.</p><p>- Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.</p><p>- You have strong verbal and written communication skills that support effective collaboration with our global engineering team.</p><p>- This position requires either equivalent practical experience or a Bachelor’s degree in a related field</p></div><p><br>Pay Grade - L<br>Equity Grade - 6<br><br>Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.<br><br>Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)<br><br>USA base pay range (CA, WA, NY, NJ, CT) per year: $165,000 - $225,000<br>USA base pay range (all other U.S. states) per year: $146,000 - $206,000<br><br>#LI-Remote</p><div class="content-conclusion"><div><p><strong>Remote-first with flexibility built in</strong><strong><br></strong>Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience.</p><p><strong>Benefits designed for you</strong><strong><br></strong>Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights:</p><ul><li>Health coverage at no cost: We cover 100% of premiums for employees and their dependents.</li><li>Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you.</li><li>Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to.</li><li>Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount.</li></ul><p>We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help.</p><p>For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law.</p></div><div><p>By clicking "Submit Application," you acknowledge that you have read Affirm's <a href="https://www.affirm.com/global-candidate-privacy-notice" target="_blank">Global Candidate Privacy Notice</a> and consent to the use of your personal information as described.</p></div></div>