Machine Learning Engineer, Frontline Experience San Francisco
Frontline Experience improves our users’ end-to-end experiences with Stripe, and helps improve Stripe based on those interactions.
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 the user-facing teams at Stripe mission critical: these teams provide fast, accurate answers in the context of our users’ businesses across phone, email, and chat.
The Frontline Experience engineering team works side-by-side with our user-facing teams to make this possible. We build products to improve our users’ end-to-end experiences with Stripe, from how users contact us to the tools we use to answer them. We use data collected through these support products and tools to continuously improve our products.
We believe in individual enablement and build tools for power users: our fellow Stripes! Our workflow platform allows other Stripes to roll out instrumented content to the entire support team, making sure best practices are adopted efficiently even as we scale. This platform is a rich source of labeled observations from each support interaction that, combined with contextual data, has the potential to power applications that could make a meaningful impact to the quality and speed of our customer interactions. As one of the first Machine Learning engineers on Leverage, you would be able to make use of this untapped data to significantly shape the direction of both our tooling and the interfaces through which users contact Stripe.
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 a production environment
- Have minimum of 4 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
- Thrive in a collaborative environment involving different stakeholders and subject matter experts.
- Take pride in working on projects to successful completion involving a wide variety of technologies and systems.
- Are comfortable working directly with your users
You might work on:
- Designing machine learning platforms and pipelines for training and running machine learning models that improve the efficiency and accuracy of our support operations. This could involve:
- Building a predictive model that uses the context of the conversation and user to help us fast track a user to the best channel (phone, chat, or email) and person for help.
- Improving the accuracy of our interactions by building a workflow suggestion model that helps surface the most relevant workflow for any given question, which is especially important as our content grows in size.
- 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 new model types into our scoring infrastructure.
We look forward to hearing from you.