Machine Learning Engineer, Payment Intelligence
The Payment Intelligence group is responsible for optimizing each of the billions of dollars of transactions processed by Stripe each year on behalf of our users, in order to maximize successful transactions while minimizing payment costs and fraud. We own products like Radar from end to end and work across the technical stack: from crafting machine learning models over our users’ data, to integrating ML intelligence and serving real-time predictions as part of Stripe’s payment infrastructure, to building user-facing product surfaces like dashboards and controls.
Stripe builds economic infrastructure for the internet, supporting businesses worldwide ranging from fledgling upstarts to Fortune 500s. These businesses place significant trust in Stripe to accelerate their success. This makes our user-facing teams’ mission critical: these teams provide fast, accurate answers in the context of our users’ businesses across phone, email, and chat.
Design machine learning platforms and pipelines for training and running machine learning models that improve the efficiency of transactions on Stripe. This could involve:
- Building prediction models for new aspects of transaction outcomes, like whether we expect to win a dispute given auto-submitted evidence
- Improving the accuracy of our prediction models for transaction outcomes, like whether a payment will be accepted or declined by the card network, or disputed as fraudulent by a cardholder
- Understanding our users’ business needs in order to evaluate model performance and improve the value model we use to evaluate transaction outcomes
- Developing and evaluating new model architectures which improve the accuracy of our prediction models
- Incorporating new features and sources of data
- Writing simulation code using Scalding to run MapReduce jobs on our Hadoop cluster to help us understand what would happen across different segments if we changed how we action our models
- Integrating new models and behaviors into Stripe’s core payment flow
- Collaborating with our machine learning infrastructure team to build support for new model types into our scoring infrastructure
We’re looking for someone who has:
- An advanced degree in a quantitative field (e.g. stats, physics, computer science) and some experience in software engineering in a production environment
- 4+ years industry experience doing software development on a data or machine learning team
- Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis
- The ability to thrive in a collaborative environment involving different stakeholders and subject matter experts
- Pride in working on projects to successful completion involving a wide variety of technologies and systems
- Comfort working directly with your users
What’s it like to work at Stripe?
Stripe is helping the internet fulfill its potential as a platform for economic progress by building software tools that accelerate global economic access and technological development. Stripe makes it easy to start, run and scale an internet business from anywhere in the world.
Stripe is, at its heart, an engineering company. To provide a missing pillar of core internet infrastructure, we hire people with a broad set of technical skills (and from a wide variety of backgrounds) who are ready to take on some of the most challenging problems in the industry – from reliably handling 100M API requests per day, to building adaptive machine learning as a result of years of data science and infrastructure work, and enabling entrepreneurs worldwide to start a global internet business.
We look at Stripe as a constant work in progress and the same is true of our people; for all of us, we believe the best is yet to come. We’re here to support each other in our curiosity and creativity – which we pursue through thoughtful discussion and knowledge-sharing among a diverse set of peers and colleagues.
We encourage all engineers to transition teams once every year and a half and also take on short-term projects with other teams across Stripe. This enables engineers to learn how different parts of Stripe work while also establishing stronger ties and cross-pollination between groups.
We contribute to existing open-source projects and the people working on them, and we release several tools as open-source. We want to work in a company of warm, inclusive people who treat their colleagues exceptionally well. The kind of people who are committed to going out of their way to help other Stripes in the short-term and pushing them to improve over the long-term (by helping them to get better at what they do).
We’re a highly cross-functional organization and view that as part of the fun: we design our space to encourage as much collaboration as possible. We have long tables in the kitchen for a reason (to enable everyone to meet new people and learn from them). We also have a culture of transparency that we carry through to email communication, ensuring that Stripes all around the world have the information they need to make good local decisions.
In both our products and our people, we aim to reflect, represent and advocate for all of our users, globally. Our users transcend geography, culture and language; what we share, collectively, is a drive to create a fairer, more economically interconnected world.
You should include these in your application:
- A short introduction describing who you are and what you’re looking for. What projects have you enjoyed working on? Which have you disliked? What motivates you?
- Links to online profiles you use (e.g. GitHub)
- A description of your work history (whether as a resume, LinkedIn profile, or prose)
At Stripe, we're looking for people with passion, grit, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Stripe, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us.