Machine Learning Engineer, Stripe

• 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
• We’re looking for someone who can bring new ideas to the table on building models able to push the state of the art at Stripe, especially within the regulatory and operational constraints of a financing business
• 5+ 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
• Hands-on experience in collaborating across multiple teams, especially Data Science and Risk Management teams

Desirable

• As a machine learning engineer for Stripe Capital, you will be responsible for designing, building, training, evaluating, deploying, and owning ML models in production with the goals of providing financing opportunities to as many users as possible while satisfying financial performance goals
• You will work closely with software engineers, data scientists, product managers, and risk managers to operate Stripe’s ML powered systems, features, and products
• You will also contribute to and influence ML architecture at Stripe and be a part of a larger ML community
• Design state-of-the-art ML models and large scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints
• Design systems to speed up the time from idea to deployment of new models
• Experiment and iterate on ML models (using tools such as PyTorch and TensorFlow) 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