Machine Learning Engineer

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 Payments organization 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 Adaptive Acceptance end-to-end, developing Machine Learning models, building fast and scalable services and creating intuitive user experiences. We serve real-time predictions as part of Stripe's payment infrastructure and architect controls that leverage ML to optimally manage users' business.

What you'll do

As a Machine Learning Engineer, you will design and build platforms and services that are configurable and scalable around the globe. You will partner with many functions at Stripe, with the opportunity to both work on infrastructure/platform systems, as well as produce direct user-facing business impact.

Responsibilities

• Design Machine Learning systems and pipelines for training and running Machine Learning models that improve the efficiency of transactions on Stripe. This could involve:
  
• Building prediction models for new aspects of transaction outcomes, like whether we expect to win a dispute given auto-submitted evidence.
  
• Improving the accuracy of our prediction models for transaction outcomes, like whether a payment will be accepted or declined by the card network, or disputed as fraudulent by a cardholder.
  
• Understanding our users' business needs in order to evaluate model performance and improve the value model we use to evaluate transaction outcomes.
  
• Developing and evaluating new model architectures which improve the accuracy of our prediction models.
  
• Incorporate new features and sources of data.
  
• Writing simulation code on our distributed clusters to help us understand what would happen across different segments if we changed how we action our models.
  
• Integrating new models and behaviors into Stripe's core payment flow.
  
• Collaborating with our Machine Learning infrastructure team to build support for new model types into our scoring infrastructure.
  
• Mentor engineers earlier in their technical careers to help them grow
  

Who you are

We're looking for people with a strong background and passion in building successful backend systems, services and APIs that deliver impactful product values to our customers. You are comfortable in dealing with changes. You love to take initiatives, and bias towards action.

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.

• 5+ years years industry experience doing software development on a data or Machine Learning team
  
• An advanced degree in a quantitative field (e.g. stats, physics, computer science) and some experience in software engineering in a production environment.
  
• Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis.
  
• The ability to thrive in a collaborative environment involving different stakeholders and subject matter experts.
  
• Pride in working on projects to successful completion involving a wide variety of technologies and systems.
  
• Comfort working directly with your users.
  
• Empathy with users and a strong customer focus
  
• Enjoyment in working with a diverse group of people with different expertise
  

Preferred qualifications

• Experience with or interest in ML, which powers many of the products we own
  
• Experience in payments and/or fraud
  

Our Chicago, Dublin, and Singapore teams spend at least 50% of their time in their local offices. We believe that more in-person interactions will contribute to better results for our employees and Stripe. Increased office time allows for improved collaboration, efficiency, and informal learning opportunities, fostering a stronger community and building our culture. We will apply the insights gained from these initial three locations to continue refining our hybrid approach.

The annual salary range for this role in the primary location is €86,400 - €129,600. This range may change if you are hired in another location. For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate's experience, qualifications, and specific location. Applicants interested in this role and who are not located in the primary location may request the annual salary range for their location during the interview process.

Specific benefits and details about what compensation is included in the salary range listed above will vary depending on the applicant's location and can be discussed in more detail during the interview process. Benefits/additional compensation for this role may include: equity, company bonus or sales commissions/bonuses; retirement plans; health benefits; and wellness stipends.

At Stripe, we're looking for people with passion, grit, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out-and set you apart-especially if your career has taken some extraordinary twists and turns. At Stripe, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us.