---
title: 'Machine Learning Engineer at Stripe'
canonical: 'https://feeny.ai/job/machine-learning-engineer-stripe-toronto-gzpt104dsemn'
type: 'job'
last_seen: '2026-10-18'
---

# Machine Learning Engineer at Stripe

- **Company:** Stripe
- **Location:** Toronto, Canada
- **Posted:** 2026-07-10
- **Last confirmed live:** 2026-10-18
- **Apply:** https://stripe.com/jobs/search?gh_jid=8014859

## 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

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](https://stripe.com/sessions/2024/a-blueprint-for-ai-acceleration).

## 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.

## Minimum requirements

- 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

## About Stripe

## Core Business
- Primary industry: Financial services / payments infrastructure
- Target customers: B2B – from startups to global enterprises (e.g., Le Monde, trucking companies)
- Mission: “Increase the GDP of the internet” by making online commerce easier and more accessible

## Products & Services
- **Stripe Payments**: Accept and optimise payments globally – online and in person; supports 135+ currencies and payment methods [stripe.com](https://stripe.com/)
- **Stripe Billing**: Manage subscriptions, invoices, and recurring revenue; over 200 million active subscriptions managed [stripe.com](https://stripe.com/)
- **Stripe Connect**: Embed payments into platforms and marketplaces
- **Stripe Atlas**: Incorporate a company, get everything needed to fundraise and start selling
- **Stripe Terminal**: In-person payments with customisable card readers
- **Stripe Issuing**: Create and manage a card issuing programme
- **Stripe Climate**: Automatically contribute a fraction of revenue to carbon removal

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private company)
- **Key Metric**: $1.9 trillion in payments volume processed in 2025 [stripe.com](https://stripe.com/)
- **Notable Investors/Partners**: Not disclosed in provided sources
- **Growth Signals**:
  - Infrastructure underpins more than 1% of global GDP [stripe.com](https://stripe.com/jobs/life-at-stripe)
  - Over 8,000 employees across 25+ offices worldwide [stripe.com](https://stripe.com/jobs)
  - 99.999% historical uptime for Stripe services [stripe.com](https://stripe.com/)
  - Deploys 10,000+ times per day while maintaining five 9s of reliability [stripe.com](https://stripe.com/jobs/life-at-stripe)
  - 135+ currencies and payment methods supported [stripe.com](https://stripe.com/)

## Competitive Advantages
- **Scale & Reliability**: Processes massive transaction volumes with 99.999% uptime – a level of trust few competitors match.
- **Developer‑First Experience**: Obsessive focus on API design, documentation, and ease of integration; “start with a blank screen, work until a product is ready, polish until exceptional” [stripe.com](https://stripe.com/jobs/life-at-stripe).
- **Global Reach**: Supports 135+ currencies and payment methods, with local offices in 25+ cities to serve regional needs.
- **Network Effects**: The more businesses use Stripe, the more data and infrastructure advantages accrue, making it harder to switch.

## Strategic Focus
- **AI & Agentic Commerce**: Building economic infrastructure for AI, including monetisation through agentic commerce [stripe.com](https://stripe.com/).
- **Crypto & Stablecoins**: Enabling borderless money movement with stablecoins and crypto [stripe.com](https://stripe.com/).
- **Platform Expansion**: Continuing to add financial tools (billing, issuing, terminal, climate) that work individually or together.
- **Globalisation**: Deepening presence in markets like Japan, India, and Latin America.

## Why Work Here
- **Culture**: “Intense and kind” – high expectations paired with a supportive, transparent environment. Weekly all‑hands with senior leaders; decisions based on idea quality, not seniority [stripe.com](https://stripe.com/jobs/life-at-stripe).
- **Growth & Ownership**: “Promotions are tied to demonstrated impact, not tenure.” Employees get substantial responsibility early, often working outside their domain [stripe.com](https://stripe.com/jobs).
- **Work Model**: Hybrid/remote‑friendly – two main HQs (San Francisco & Dublin) plus 25+ offices, and many Stripes work from home or coworking spaces [stripe.com](https://stripe.com/jobs).
- **Benefits**: Comprehensive medical, mental health, financial advisory, family planning, and parental leave. Education team provides onboarding and ongoing expert‑led courses [stripe.com](https://stripe.com/en-gi/jobs/benefits).
- **Diversity & Inclusion**: Employee‑created resource groups (Stripe Communities), accommodations for disabilities, and a commitment to equal opportunity [stripe.com](https://stripe.com/jobs).

## Sources
1. [stripe.com/jobs/life-at-stripe](https://stripe.com/jobs/life-at-stripe)
2. [stripe.com/jobs](https://stripe.com/jobs)
3. [stripe.com/](https://stripe.com/)
4. [stripe.com/jobs/search](https://stripe.com/jobs/search)
5. [stripe.com/en-gi/jobs/benefits](https://stripe.com/en-gi/jobs/benefits)

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