Machine Learning Engineer, Stripe

• 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
• Have at least 3 years of experience shipping ML systems in production
• Hold yourself and others to a high bar when working with production systems
• Take pride in taking ownership and driving projects to business impact
• Thrive in a collaborative environment
• 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

Desirable

• Our Applied ML team aims to reform how our users interact with Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks
• Some examples include helping our users resolve issues with Stripe faster or making it easier for our users to sign up and navigate Stripe. We are using the latest LLMs as well as fine-tuning our own models
• We're an end-to-end team going from ideas to models to shipping in production
• As a machine learning engineer, you will be responsible for analyzing opportunities, proposing ideas, training & evaluating ML models, running experiments, and deploying everything to production
• You will also have the opportunity to contribute to and influence ML architecture at Stripe as well as be a part of a larger ML community
• Our team operates fluidly and here are some problems you may tackle:
• How do we evaluate a system offline & online?
• How do we improve performance to match (and beat) humans?
• How do we ensure model quality doesn’t degrade online?
• Does fine-tuning an LLM give us better performance?
• What are the right OSS and in-house platforms we should invest in?
• 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