Machine Learning Engineer, Supportability

Overview 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. Machine Learning at Stripe Machine learning is an integral part of almost every service at Stripe. Key products and use-cases powered by ML at Stripe include merchant and transaction risk, payments optimization and personalization, identity verification, and merchant data analytics and insights. We are also using the latest generative AI technologies to re-imagine product experiences, and are developing AI Assistants both for our customers and to make Stripes more productive across Support, Marketing, Sales, and Engineering roles within the company. Stripe handles over $1T in payments volume per year, which is roughly 1% of the world’s GDP. We process petabytes of financial data using our ML platform to build features, train models, and deploy them to production. We use a combination of highly scalable and explainable models such as linear/logistic regression and random forests, along with the latest deep neural networks from transformers to LLMs. Some of our latest innovations have been around figuring out how best to bring transformers and LLMs to improve existing models and enable entirely new product ideas that are only made possible by GenAI. Stripe’s ML models serve millions of users daily and reduce financial risk, increase payment success rate, and grow the GDP of the internet. We work on challenging problems with large business impact, and seek to foster creativity and innovation. What you’ll do As a machine learning engineer in Supportability, 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 Drive the expansion of Stripe's largest LLM-based system, scaling its usage and integrating new capabilities through agentic approaches or supervised learning. Rapidly prototype new ML-based approaches to achieve key business goals. 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 Explore cutting-edge ML techniques and evaluate their potential to solve business problems Identify high-impact opportunities, and drive the long-term ML roadmap through well-scoped high-leverage initiatives 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 and evaluating 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 2+ 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 Experience rigorously evaluating model performance, including cleaning data, and working with data-generating processes to improve signal and reduce noise in high-noise datasets. Proficiency in creatively applying modern machine learning techniques and Generative AI models to solve complex business problems. Preferred requirements 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 with online experimentation such as A/B testing or multi-armed bandits. Experience with model calibration #J-18808-Ljbffr