Staff Machine Learning Engineer, Credit Decisions

US / Canada

Full Time Senior-level / Expert USD 45K - 150K *
Stripe logo

Stripe

Online payment processing for internet businesses. Stripe is a suite of payment APIs that powers commerce for online businesses of all sizes, including fraud prevention, and subscription management. Use Stripe’s payment platform to accept and ...

View all employer listings

Apply now Apply later

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

Stripe’s mission is to increase the GDP of the internet and build its economic infrastructure. Credit Decisions brings together machine learning with product development and business expertise to lower Stripe’s credit risk at scale, while retaining a best in class user experience. Achieving this goal is critical to Stripe’s long term growth and profitability. We protect Stripe’s brand while also protecting the company from credit losses that can greatly put our financial position at risk.

What you’ll do

The Credit Decisions team consists of machine learning and backend engineers who want to tackle this problem through creative new product ideas and impactful machine learning models. We work closely with our credit partners in product, business, data science, and operations to prioritize and drive our shared strategy. We are continuously exploring and undertaking new ideas and as a Staff ML Engineer you can have an outsized impact on the future of how Stripe manages risk at scale. 

Responsibilities

  • Set a technical direction for how we balance financial risk and user experience while managing credit risk at scale, in collaboration with your manager and cross functional leadership
  • Set and execute a vision for incorporating new advances in machine learning in ways that best achieve the team’s business objectives
  • Design, train, evaluate, improve, and launch models that identify optimal actions in difficult tradeoff scenarios
  • Debug production issues across services and multiple levels of the stack
  • Collaborate across different ML teams including ML infra to continuously improve ML development velocity and capabilities at Stripe
  • Support team members in delivering a high level of technical quality 

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

  • Have 7+ years of machine learning engineering experience
  • Have led multiple engineers in delivering large, high impact projects
  • Have had experience shipping ML models in a large scale production environment
  • Have had experience working with Spark to condense large data sources into insights to drive decision making
  • Enjoy working in a fast paced collaborative environment involving different partners and subject matter experts
  • Hold yourself and others to a high bar when working with production systems
  • Thrive on a high level of autonomy and responsibility and have a bias toward impact

* Salary range is an estimate based on our salary survey at salaries.ai-jobs.net

Tags: Credit risk Engineering Machine Learning ML ML models Spark

Perks/benefits: Career development Startup environment

Region: North America
Country: Canada
  • Share this job via
  • or

Other jobs like this

Explore more AI/ML/Data Science career opportunities

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.