Software Engineer, Machine Learning Infrastructure

EngineersOfAI; Toronto; Hybrid; CAD 110,000 - 170,000; Full time

Job summary

Stripe is building the next generation of ML Infra services to accelerate ML development velocity across the company. You will work with machine learning engineers, data scientists, and product engineers to enable end-to-end solutions in data, analytics, and AI/ML platforms.

You will design scalable services for notebooks, model training, experimentation, serving, and LLM applications across multiple regions, prioritizing reliability and low latency.

Qualifications

• 2+ years of professional software development
• Experience with service-oriented architectures and large-scale distributed systems
• Experience with production ML platforms or LLM applications
• Experience running operations for high availability, low latency systems
• Ability to collaborate with multiple teams to drive outcomes

Responsibilities

• Designing and building scalable, reliable services for notebooks, ML model training, experimentation, serving, and LLM applications across regions
• Creating libraries that enable ML engineers to move from experimentation to production
• Working with product teams and ML engineers to improve day-to-day productivity
• Taking ownership of technical and product challenges across diverse systems and technologies

Skills

Software development

Distributed systems

MLOps

LLM applications

Production deployment

Job description

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. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

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 Infra team builds services and tools that power every step in the ML lifecycle, including data exploration, feature generation, experimentation, training, deploying, serving ML models, and building LLM applications. With the phenomenal developments happening in the field of AI, we are positioned to accelerate the adoption of AI/ML across all parts of the company by building highly scalable and reliable foundational infrastructure.

What you’ll do

You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company.

Responsibilities

• Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions.
• Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems.
• Working directly with product teams and ML engineers to improve their day-to-day productivity.
• Taking ownership of and finding solutions for technical and product challenges by working with a diverse set of systems, processes, and technologies.

Who you are

We’re looking for people with a strong background or interest in building successful products or systems; you’re passionate about solving business problems and making impact, you are comfortable in dealing with lots of moving pieces; and you’re comfortable learning new technologies and systems. You are comfortable working with other Stripe teams across the US and Canada.

Minimum requirements

• 2+ years of professional software development experience with a solid background on service oriented architecture and large-scale distributed systems
• Experience working through the full life cycle of software development, from talking to users, to design and implementation, to testing and deployment, to operations
• Experience working on production ML platforms, MLOps solutions, or building LLM applications
• Experience running operations for high availability, low latency systems
• Experience partnering with other teams to drive business outcomes
• A sense of pragmatism: you know when to aim for the ideal solution and when to adjust course

Preferred qualifications

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