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
• Bachelor's Degree in ML/AI or related field (e.g. math, physics, statistics)
• You have a strong technical background, including 5+ years of experience 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, evaluating & productionizing machine learning models at scale

Desirable

• As a Machine Learning Engineer on the Support Experience team, you'll play a crucial role in enhancing our self-serve support experiences. You will be responsible for designing, building, training, evaluating, deploying, and owning ML models in production.
• For example, we apply LLMs to answer user questions with conversational agents and personalize product documentation, and are building automated systems to solve complex user problems.
• 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
• Design and implement state-of-the-art ML models and large scale ML systems for enhancing self-serve support capabilities, balancing ML principles, domain knowledge, and engineering constraints
• Develop and optimize contextual conversation models and ML-powered resolution flows for common support scenarios, using tools such as PyTorch, TensorFlow, and XGBoost
• Create and refine pipelines for training and evaluating models in both offline and online environments, with a focus on improving support quality and user satisfaction
• Implement ML features that streamline information collection and processing for support agents, enhancing overall support efficiency
• Collaborate with product, strategy, and content teams to propose, prioritize, and implement new AI-driven support features and improve answer capabilities
• Stay current with the latest developments in ML/AI, particularly in natural language processing and conversational AI, and apply innovative ideas to improve support experiences