Software Engineer, Machine Learning Infrastructure

Monograph; Toronto; On-site; CAD 80,000 - 110,000; Full time

Job summary

A leading financial infrastructure company located in Toronto is seeking a software engineer to develop scalable ML Infra services. The ideal candidate has over 2 years of software development experience, particularly in MLOps and distributed systems. You will work closely with product teams to enhance AI and ML capabilities. This role offers a chance to impact critical business solutions with emerging technologies.

Qualifications

• 2+ years of experience in software development.
• Experience in full software development lifecycle.
• Experience with ML platforms or LLM applications.

Responsibilities

• Design and build scalable ML services.
• Create libraries for seamless ML transitions to production.
• Work with product teams to improve productivity.

Skills

Software development

Service oriented architecture

Distributed systems

MLOps solutions

Machine learning platforms

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 be 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