Machine Learning Engineer, Payments ML Accelerator
Stripe
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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
The Payments ML Accelerator team is developing capabilities that will unlock the proliferation of ML based techniques across Stripe’s payment products. We are developing cutting edge deep learning models tailored to Stripe’s payment data, and infrastructure to enable rapid ML exploration and very fast experiment cycles. We are exploring novel applications powered by ML as well as improving core product features such as detecting fraudulent transactions across all payment methods or optimizing payment acceptance rate.
What you’ll do
As a machine learning engineer, you will design and build platforms and services that are configurable and scalable. You will have the opportunity to build and deploy advanced ML applications and generalizable feature engineering pipelines, with the aim to produce business impact and raise the bar for technical excellence. You will also have the opportunity to contribute to and influence ML architecture at Stripe.
Responsibilities
- Build and deploy deep learning architectures and feature embeddings for Payment entities such as merchant, issuer, or customer
- Develop DNN applications and establish the foundation to facilitate increased DNN adoption at Stripe
- Design and architect generalizable ML workflows for rapid expansion of existing ML solutions
- Experiment with advanced ML solutions in the industry and ideate on product applications
- Collaborate with our machine learning infrastructure team to leverage new infra services for business solutions
- Collaborate with data scientists to build ML models
Who you are
We are looking for ML Engineers who are passionate about using ML to improve products and delight customers. You have experience developing streaming feature pipelines, building ML models, and deploying them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action.
Minimum requirements
- At least 5 years of industry experience doing end-to-end ML development on a machine learning team and bringing ML models to production
- Advanced degree in a quantitative field (e.g. computer science, statistics, physics, …)
- Proficient in Python, Scala, Spark
Preferred qualifications
- Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis.
- Experience evaluating niche and upcoming ML solutions
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.
The annual US base salary range for this role is $211,200 - $316,800. 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 location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process.
Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends.
Office locations
South San Francisco HQ, New York, or Seattle
Remote locations
Remote in United States
Team
Data & Data Science
Job type
Full time
This job is no longer accepting applications
See open jobs at Stripe.See open jobs similar to "Machine Learning Engineer, Payments ML Accelerator" Glynn Capital.