机器学习工程师 II（承销机器学习）
Machine Learning Engineer II (Underwriting ML)
在Affirm，我们存在的意义是为那些重要的时刻提供清晰、可预测的支付方式，没有隐藏费用，没有意外，也没有在最重要的事情上的妥协。
在承销机器学习团队中，你将构建和改进机器学习系统，用于实时交易决策，评估每个Affirm结账的还款风险和预期价值。你将与经验丰富的机器学习工程师、平台合作伙伴和跨职能利益相关者紧密合作，将模型从想法转化为原型，再投入生产，并随着用户行为和宏观经济状况的变化，通过强大的度量和监控保持其健康状态。
你将负责：
- 使用表格和序列数据的混合方法开发和迭代承销预测模型
- 从专有和第三方信号构建和扩展特征管道和训练数据集，在需要时与数据和平台团队合作
- 原型化新的建模思路和功能，运行离线实验，并将表现最佳的方法带入生产环境，同时设置适当的风控措施
- 协助将模型投入生产：集成到批量和/或实时决策系统中，并提高可靠性、延迟和操作稳健性
- 实现模型和数据健康状况的监控，并帮助定义重新训练/回测流程
- 与工程、风险分析、产品和机器学习平台团队协作，定义需求，评估权衡，并向技术和非技术受众清晰地传达结果
我们寻找：
- 具有2年以上机器学习工程师经验或相关领域的博士学位
- 强大的Python技能和编写生产级代码的经验
- 有构建和评估分类问题模型的经验（优先考虑梯度提升决策树如LightGBM/XGBoost/CatBoost，或其他类似模型）
- 有使用深度学习框架的经验（PyTorch优先）
- 有使用分布式数据处理或并行计算框架的经验（Spark优先；Ray/Dask或其他类似工具）
- 有使用机器学习生命周期工具进行训练编排、实验和模型监控的经验（例如Kubeflow、Airflow、MLflow或类似的内部平台）
- 熟练使用AI驱动的开发者工具（例如Claude Code、Cursor或其他类似工具）以加速迭代、调试和代码质量，作为日常开发的一部分
查看英文原文
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. 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.
What you’ll do
- You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data
- You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.
- You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
- You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness.
- You will instrument and monitor model and data health, and help define retraining/backtesting workflows
- 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.
What we look for
- You have a total of 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 (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar).
- Experience with a deep learning framework (PyTorch preferred).
- Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar).
- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).
- 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.
- 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.
- You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.
- Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.
- You have strong verbal and written communication skills that support effective collaboration with our global engineering team.
- This position requires either equivalent practical experience or a Bachelor’s degree in a related field
Pay Grade - L
Equity Grade - 6
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.
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.)
USA base pay range (CA, WA, NY, NJ, CT) per year: $165,000 - $225,000
USA base pay range (all other U.S. states) per year: $146,000 - $206,000
#LI-Remote
Remote-first with flexibility built in
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.
Benefits designed for you
Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights:
- Health coverage at no cost: We cover 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 that are right for you.
- Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to.
- Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount.
We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help.
For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law.
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