Staff Machine Learning Engineer, Payment Intelligence
Seattle, Remote (North America)
The Payment Intelligence group optimizes each of the billions of dollars of transactions processed by Stripe annually on behalf of our users, maximizing successful transactions while minimizing payment costs and fraud. We own products like Radar end-to-end: from developing machine learning models, to building performant and scalable systems and services, and creating user-facing products that enable our users to combat fraud and give them insights into how their business is performing. We serve real-time predictions as part of Stripe’s payment infrastructure and architect controls that leverage ML to optimally manage users’ business.
We’re looking for people with a strong quantitative background and passion for designing and building production machine learning models and systems that deliver impactful product values to our customers. You are comfortable working in a rapidly evolving environment and product landscape and take initiative, biasing toward action.
- Create long term technical vision for the org, and identify paths to deliver business value in shorter term phases
- Enhance and implement machine learning systems to enable rapid model development, productionization, and iteration
- Develop and improve the performance of machine learning models through featurizing new data sources and types of data, as well as tuning and re-architecting models
- Design and implement online and offline model evaluation and connect model performance to business outcomes
- Integrate machine learning predictions into Stripe’s products and existing systems
- Make significant hands-on contributions to deliver critical projects and bring value to customers
- Lead by example to uphold high engineering standards, and elevate quality and engineering efficiency across Stripe
- Collaborate with stakeholders across the organization including dependency engineering teams, product, design, infrastructure, and operations
- Mentor engineers in their technical careers to help them grow
We’re looking for someone who has:
- At least 7 years of experience developing quantitative models in academia or industry and at least 3 years of professional software development experience
- Strong experience in successful delivery of high quality machine learning models and systems in a large-scale production environment
- Experience leading technically complex projects that involve multiple engineering and data science teams
- Demonstrated experience in upleveling engineering best practices and creating technical efficiencies across teams
- Thrived in a collaborative environment involving different stakeholders and subject matter experts
- Enjoyment in working with a diverse group of people with different expertise
Nice to haves:
- Experience in payments and/or fraud
You should include these in your application:
- Your resume and/or LinkedIn profile
- A 1-2 paragraph summary of your favorite project from any of your work or personal experiences
What’s it like to work at Stripe?
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 from various backgrounds with broad technical skills. Stripes 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 empowering 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. 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 contribute to open-source projects and the people working on them, and we release tools as open-source.
We want to work in a company of warm, inclusive people who treat their colleagues well. The kind of people who commit 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 diverse organization and view that as part of the fun: we design our space to encourage as much collaboration as possible. 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.