# Staff Software Engineer, Payments and Applied Machine Learning

**Company:** [Stripe](https://hotfix.jobs/companies/stripe)
**Location:** Seattle, WA, New York, NY, San Francisco, CA
**Role:** Engineering Management
**Experience:** 10+ years
**Skills:** Distributed Systems, API Design, Java, Ruby, Python, TypeScript, Kafka, Flyte, Airflow, MongoDB, Model Serving, Training Pipelines, Observability, Fraud Detection
**Posted:** 2026-09-01

> Staff engineer defining technical strategy and building payment intelligence products across infrastructure, APIs, frontend, and ML-adjacent systems. Requires 10+ years of software engineering experience, strategic technical leadership, and expertise in distributed systems and user-facing products.

## Job Description

## Responsibilities
- Define technical strategy for multiple experiences across the Payment Intelligence portfolio, with a focus on quality and performance.
- Establish standards, tooling, and processes that support high-quality code delivery at scale.
- Partner with major merchants to co-build payment-centric intelligence solutions.
- Collaborate with product, design, and machine learning teams to deliver software.
- Work across the stack, contributing to infrastructure, foundational systems, frontend, APIs, and ML-adjacent functionality.

## Requirements
- 10+ years of software engineering experience.
- 5+ years of experience in a strategic technical leadership role.
- Experience leading engineering teams working on distributed systems, API design, and user-facing products.
- Proven record of delivering pragmatic solutions that accelerate business growth.
- Ability to drive projects at a high level while contributing hands-on to technical solutions.
- Strong communication and cross-team, cross-organization, and cross-functional collaboration skills.

## Nice-to-haves
- Experience with payment systems and/or fraud detection.
- Familiarity with production ML systems, including model serving, training pipelines, observability, and evaluation.
- Experience building 0-to-1 products and production systems.
- Experience with a range of software languages and frameworks, including Java, Ruby, Python, TypeScript, Kafka, Flyte, Airflow, and MongoDB.
- Ability to navigate ambiguity and make technical decisions with incomplete information.

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