S Machine Learning Engineer Stripe via Greenhouse Toronto 8212 ml foundations View role

Nubeero Limited; Toronto; On-site; CAD 120,000 - 180,000; Full time

Job summary

Stripe is looking for a machine learning engineer to analyze opportunities, train and evaluate models, and deploy them to production. You will build pipelines, experiment with models, and contribute to scalable ML architectures across systems. You’ll collaborate with product teams to shape features and stay ahead of ML/AI developments.

You will work on production-grade ML systems, handle ambiguity, and drive impact by delivering robust, scalable solutions in a fast-paced environment.

Qualifications

• At least 3 years shipping ML systems in production.
• Experience developing streaming feature pipelines.
• Experience deploying ML models to production and backend integration.
• Ability to work in ambiguous environments and take initiative.

Responsibilities

• Develop pipelines and automated processes to train and evaluate models in offline and online environments.
• Integrate ML models into production systems and ensure their scalability and reliability.
• Collaborate with product and strategy partners to propose, prioritize, and implement new features.
• Engage with the latest ML/AI developments and translate ideas into productionized solutions.

Skills

ML Engineering

Production ML

Streaming pipelines

Deployment to production

Job description

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

Our Applied ML team aims to reform how our users interact with Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include helping our users resolve issues with Stripe faster or making it easier for our users to sign up and navigate Stripe. We are using the latest LLMs as well as fine-tuning our own models. We're an end-to-end team going from ideas to models to shipping in production. You can learn more about our team’s work from this recent talk .

What you’ll do

As a machine learning engineer, you will be responsible for analyzing opportunities, proposing ideas, training & evaluating ML models, running experiments, and deploying everything to production. You will also have the opportunity to contribute to and influence ML architecture at Stripe as well as be a part of a larger ML community.

Responsibilities

Our team operates fluidly and here are some problems you may tackle:

• How do we evaluate a system offline & online?
• How do we improve performance to match (and beat) humans?
• How do we ensure model quality doesn’t degrade online?
• Does fine-tuning an LLM give us better performance?
• What are the right OSS and in-house platforms we should invest in?

And in the process you will:

• Develop pipelines and automated processes to train and evaluate models in offline and online environments
• Integrate ML models into production systems and ensure their scalability and reliability
• Collaborate with product and strategy partners to propose, prioritize, and implement new product features
• Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions

Who you are

We are looking for ML Engineers who are passionate about using ML to improve products and delight customers. You have experience developing streaming feature pipelines, building ML models, and deploying them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action.

• Have at least 3 years of experience shipping ML systems in production
• Hold yourself and others to a high bar when working with production systems
• Take pride in taking ownership and driving projects to business impact
• Thrive in a collaborative environment

Preferred qualifications

• 5+ years of experience in full time software development roles
• Experience shipping LLM integrations to user products with high quality
• Experience operating in highly ambiguous environments
• Knowledge about driving a hypothesis from data