What you’ll do

Stripe’s mission is to build the economic infrastructure for the internet.  Credit Detection brings together machine learning with product development 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 put our financial position at risk.

The Credit Detection team consists of machine learning 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 an Engineering Manager you can have an outsized impact on the future of how Stripe manages risk at scale.

As a machine learning engineer, you will be responsible for designing, building, training, evaluating, deploying, and owning ML models in production. You will work closely with software engineers, machine learning engineers, product managers, and data scientists to operate Stripe’s ML powered systems, features, and products. You will also have the opportunity to contribute to and influence ML architecture at Stripe and be a part of a larger ML community.

Responsibilities

• Design state-of-the-art ML models and large scale ML systems for detection and decisioning for Stripe products based on ML principles, domain knowledge, and engineering constraints

• Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, and XGBoost) to achieve key business goals and drive efficiency

• Develop pipelines and automated processes to train and evaluate models in offline and online environments

• Integrate ML models into production systems and ensure their scalability and reliability

• Collaborate with product and strategy partners to propose, prioritize, and implement new product features

• Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions

Who you are

We are looking for ML Engineers who are passionate about building ML systems that touch the lives of millions. You have experience developing efficient feature pipelines, building advanced ML models, and deploying them to production. You are comfortable with ambiguity, love to take initiative, have a bias towards action, and thrive in a collaborative environment.

Minimum requirements

• 6+ years of industry experience building and shipping ML systems in production

• Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark

• Knowledge of various ML algorithms and model architectures

• Hands-on experience in designing, training, and evaluating machine learning models

• Hands-on experience in productionizing and deploying models at scale

• Hands-on experience in orchestrating complicated data pipelines and efficiently leveraging large-scale datasets

Preferred qualifications

• MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics)

• Experience with DNNs including the latest architectures such as transformers and LLMs

• Experience working in Java or Ruby codebases

• Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems

• Experience in adversarial domains such as Payments, Fraud, Trust, or Safety

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.