Software Engineer, Machine Learning Infrastructure

Details

Who We Are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Stripe’s mission is to increase the GDP of the internet.

About the Team

Stripe processes over $1T in payments volume per year, which is roughly 1% of the world’s GDP. The tremendous amount of data makes Stripe one of the best places to do machine learning.

The ML Infrastructure team builds services and tools that power every step in the ML lifecycle, including data exploration, feature generation, experimentation, training, deploying and serving ML models, and building LLM applications. The team builds highly scalable and reliable foundational infrastructure to accelerate the adoption of AI and ML across the company.

Responsibilities

• Design and build scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions.
• Create services and libraries that enable ML engineers at Stripe to transition seamlessly from experimentation to production across Stripe’s systems.
• Work directly with product teams and ML engineers to improve their day-to-day productivity.
• Take ownership of and find solutions for technical and product challenges by working with a diverse set of systems, processes, and technologies.
• Work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experiences across data, analytics, and AI/ML platforms.
• Build the next generation of ML infrastructure services and major new capabilities that improve ML development velocity and MLOps maturity across the company.

Requirements

• 2+ years of professional software development experience, with a solid background in service-oriented architecture and large-scale distributed systems.
• Experience working through the full software development lifecycle, from talking to users through design, implementation, testing, deployment, and operations.
• Experience working on production ML platforms, MLOps solutions, or LLM applications.
• Experience operating high-availability, low-latency systems.
• Experience partnering with other teams to drive business outcomes.
• Pragmatism and the ability to determine when to pursue an ideal solution and when to adjust course.
• Comfortable working with other Stripe teams across the United States and Canada.

Preferred Qualifications

• Experience building and shipping production AI agents.
• Familiarity with LLMs and LLM frameworks.
• Experience training and shipping machine learning models to production to solve critical business problems.