- Set a technical direction for how we detect fraud, credit and other risks at scale at Stripe in collaboration with your manager and cross-functional leadership
- Set and execute a vision for incorporating new advances in machine learning and deep learning in ways that best achieve the team's business objectives
- Design, train, evaluate, improve, and launch models that detect and measure risks to inform optimal action in the tradeoff between user experience and expected financial or reputational risk
- 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
- 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
- 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
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Staff Machine Learning Engineer, Risk Detection - Seattle, WA, United States - Stripe
Description
Who we areAbout 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 The Risk Detection Team applies machine learning to a variety of areas with the aim to drive up profitability while reducing the financial or reputational risk associated with enabling each user on Stripe, while retaining a best in class user experience. Achieving this goal is critical to Stripe's long term growth. 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.
What you'll do We are exploring new areas and kicking off projects where you can have an outsized impact on the architecture, implementation, and design choices behind these machine learning models and systems. As a Staff ML Engineer you will be collaborating with other engineers on your team and across Stripe, as well as key partners in the product and risk organization, data science, and operations.
Responsibilities
Minimum requirements
This role is available either in an office or a remote location (typically, 35+ miles or 56+ km from a Stripe office).
Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.
A remote location, in most cases, is defined as being 35 miles (56 kilometers) or more from one of our offices. While you would be welcome to come into the office for team/business meetings, on-sites, meet-ups, and events, our expectation is you would regularly work from home rather than a Stripe office. Stripe does not cover the cost of relocating to a remote location. We encourage you to apply for roles that match the location where you currently or plan to live.
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Staff Machine Learning Engineer, Risk Detection professionals in Seattle
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Xin Liu
Applied scientist in Computer Vision/Deep Learning