Machine Learning Engineer San Francisco
Develop models and tools that power internal and user-facing machine learning applications.
We’re looking for Machine Learning Engineers who can help us build and deploy machine learning models to directly enable our fraud and risk detection systems and expand machine learning to other segments of our business. As a Machine Learning Engineer at Stripe, you will work on problems that run the gamut from data science to production engineering. You’ll work with other machine learning engineers at Stripe and partner with a diverse set of other teams, including engineers who build platform-level infrastructure or user-facing products incorporating machine learning, as well as analysts who interpret and act on our models. You will identify new approaches and methods to improve performance in our core machine learning applications and investigate new applications for machine learning as Stripe grows.
- Build machine learning models that power applications like fraud detection
- Define metrics for feature evaluation and model performance
- Analyze data and investigate different model types and parameters
- Design and implement robust data pipelines
- Own and improve production scoring systems and participate in on-call rotations, along with every member of the engineering team
You may be fit for this role if you:
- Have an advanced degree in a quantitative field (e.g. stats, physics, computer science) and some experience in software engineering in production industry environment
- Have minimum of 2 years industry experience doing software development on a data or machine learning team
- You know how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis
- Are excited about taking real-world business problems and building machine learning solutions to them, including identify appropriate approaches and techniques
You might work on:
- Working with risk analysts to take feature ideas and turn them into valuable new features in our models, quantifying the expected performance improvements and getting them into production.
- 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.
- Collaborating with our machine learning infrastructure team to build support for a new model type into our scoring infrastructure.
- Defining application-specific metrics to help us evaluate the performance of our models, and tracking the results by creating a dashboard in React.
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.#LI-KE1
We look forward to hearing from you.