Experienced Software Engineer - Payment Risk Machine Learning Infrastructure

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. #LI-Hybrid

The Payment Risk team is responsible for developing a suite of risk products for Plaid’s customers to manage risk in bank payments. This team delivers sophisticated risk predictions as well as direct signals through public APIs that customers use to understand and manage risk on their payment flows.

As a software engineer on the payment risk team, you will work on tooling and infrastructure that facilitates the development of our machine learning based risk models, from data and feature pipelines to model evaluation framework. You will also work on model serving infrastructure to support real time risk assessment based on large amounts of data at low latency. Your work will contribute to the rapid growth of an innovative product.

Responsibilities

• Build products with real impact: your work will touch tens of millions of end-users, the best applications in FinTech, and major financial institutions
• Develop tools and infrastructure for quick iteration on machine learning features and models
• Passionate about applying Machine Learning to real-world problems, especially financial fraud
• Technical leadership in engineering excellence and mentorship

Qualifications

• 4+ years of software engineering experience
• Experience in building production infrastructure for machine learning feature engineering or model training, evaluating, and deploying
• End-to-end process ownership and customer obsession