Machine Learning Engineer, Affirm
Fraud
CA$125-175k
Buy-now-pay-later card for online and in-store
Be an early applicant
Buy-now-pay-later card for online and in-store
1001+ employees
Be an early applicant
CA$125-175k
1001+ employees
Company mission
To deliver honest and transparent financial products that improve users' lives.
Role
Who you are
- 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 tabular 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
What the job involves
- On the ML Fraud team, you’ll build and improve machine learning systems that make real-time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion
- 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 fraud patterns evolve
- You will develop and iterate on fraud prediction models using a mix of approaches for tabular and behavioral 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 as fraud patterns evolve
- You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences
Our take
Affirm offers point-of-sale loans to consumers, working with merchants like Shopify, KAYAK, and Walmart. Buy now, pay later transactions have grown in popularity in recent years, and this business is tapping into the market with a totally different approach to credit - by delivering the Affirm card, the first US debit card with the option to pay-over-time.
Serving over 21 million consumers and over 300,000 merchants, the company offers flexible payback options ranging from 3 to 36 months. The interest rates run between 0% and 30%, so, while they are not necessarily the cheapest option, Affirm prides itself on having no fees for late payments, no hidden charges, and total transparency about what people will pay before they take out a loan.
Affirm collects payments from the customers and pays the merchants directly, shouldering the risk itself. The company has boosted its market share, expanding its availability beyond the US to include most of Canada, and continues to launch new features and create new partnerships towards bettering its offering.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
-11% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Sep 2020
$500m
SERIES G
Apr 2019
$300m
SERIES F
Total funding: $1.1bn
Company benefits
- Compensation: We have a simple, flexible, and transparent remote-first compensation structure so you can make the best decisions for yourself and your family
- Spending Wallets: Access tech, food, lifestyle, and family planning wallets for your expenses
- Supportive Communities: Get involved with our employee resource groups and community groups
- Remote-first Workforce: If your role is remote, you can set up shop anywhere in your home country
- Generous Time Off: Take the time you need when life happens
- Health Benefits: Get a plan that fits your needs
- Mental Healthcare: Take care of your mind with great mental health programs
- Parental Leave: Birth and non-birth parents get 18 weeks’ paid leave. Plus, a 4-week return-to-work transition program, at full base pay
- Away Days: We offer 20 company-wide paid days off—which help our teams collectively pause to recharge
- Learning & Development: Engage in exciting learning programs to level up your growth
Company HQ
Chinatown, San Francisco, CA
Leadership
Max Levchin
(Co-Founder & CEO)Has founded/co-founded numerous companies, holding various executive positions at each, including Paypal, Slide, Glow, HVF Labs, and SciFi VC. Also worked at large brands including Google, Yelp, Yahoo, and Evernote.
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