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 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.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.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.We’re looking for people with a strong background or interest in building successful products or systems; 2+ years of skilled software development experience with a solid background on service oriented architecture and large-scale distributed systemsExperience working through the full life cycle of software development, from talking to users, to design and implementation, to testing and deployment, to operationsExperience working on production ML platforms, MLOps solutions, or building LLM applicationsExperience running operations for high availability, low latency systemsExperience partnering with other teams to drive business outcomesA sense of pragmatism: you know when to aim for the ideal solution and when to adjust coursePreferred qualifications Experience building and shipping production AI agentsFamiliarity with the LLMs and LLM FrameworksExperience training and shipping machine learning models to production to solve critical business problems #

📌 Software Engineer, Machine Learning Infrastructure (Winnipeg)
🏢 EngineersOfAI
📍 Winnipeg