Stripe is hiring a Machine Learning Engineer in 8212 ML Foundations — Toronto. The overview below is synthesized from the employer posting on stripe.com: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.
Who we are
About Stripe
Company context from the listing:
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.
About the team
Company context from the listing:
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.
What you’ll do
Day-to-day scope for the Machine Learning Engineer as described in the posting:
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
Day-to-day scope for the Machine Learning Engineer as described in the posting:
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
Experience and skills the team lists as required:
The listing frames the Machine Learning Engineer at Stripe as a hands-on product role. 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.
Minimum requirements
Experience and skills the team lists as required:
- 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
Experience and skills the team lists as required:
- 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
Build a standout application
For the Machine Learning Engineer at Stripe, reviewers look for concise evidence over buzzwords. Mirror the language of the posting sparingly, quantify support or delivery outcomes, and show how you handled ambiguity, time-zone collaboration and user empathy. Keep your resume to impact, keep your cover note to one page, and link to artifacts — tickets resolved, docs shipped, dashboards owned — that prove you can operate in a fast-moving Web3 team. Prepare to discuss a time you turned a confusing user report into a clear fix and how you measure quality in support and operations.
Web3 hiring values reliability: on-time follow-through, clear writing, and a track record of improving runbooks and tooling. Treat the application as a work sample. For interviews, be ready to walk through how you prioritize across time zones, handle a difficult user, and decide when to escalate versus resolve directly. Show how you document decisions so the next teammate benefits.
In a distributed Web3 org, trust builds through written clarity. Use the cover note to demonstrate it.