Staff Software Engineer, Payments and Applied Machine Learning
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.
About the job
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.
Skills
Distributed Systems, API Design, Java, Ruby, Python, TypeScript, Kafka, Flyte, Airflow, MongoDB, Model Serving, Training Pipelines, Observability, Fraud Detection
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