• 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
• 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

Desirable

• The Machine Learning Infrastructure group at Stripe aims to provide state of the art infrastructure and support for building and operationalizing AI/ML models for all business verticals within the company, including but not limited to models that mitigate risks across Stripe’s products and services globally, and models that help our customers to fight fraud by leveraging Stripe’s user facing products like Radar and Identity
• ML is a top priority for Stripe in the coming years. With the phenomenal developments happening in the field of AI, we are positioned to accelerate the adoption of AI/ML across all parts of the company by building highly scalable and reliable foundational infrastructure
• 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
• Designing, training, improving & launching machine learning models using tools such as XGBoost, Tensorflow, PyTorch
• Proposing and implementing ideas that directly impact Stripe’s top line metrics
• Propose new feature ideas and design data pipelines to incorporate them into our models
• Improve the way we evaluate and monitor our model and system performance
• Collaborate with stakeholders and drive end-to-end projects involving a variety of technologies and systems to successful completion