Machine Learning Engineer, Growth Platform

Details

Stripe is a financial infrastructure platform for businesses. Growth Platform builds machine learning systems that help businesses discover and use Stripe products that meet their needs. Its recommendations reach users across the Dashboard, email, onboarding, documentation, and AI agent interfaces.

The team works on recommendation and ranking models, contextual bandits, agent-based recommendations, and the data and evaluation systems behind them.

Responsibilities

• Design, train, evaluate, deploy, and maintain models for recommendation, ranking, and personalized action selection across Growth Platform surfaces.
• Improve contextual bandit and policy-learning approaches, including exploration, reward design, and adaptation to user context and feedback.
• Build agent-based recommendation capabilities that use business context to identify relevant products and integration options.
• Develop reliable data and feature pipelines for training and inference, improving data freshness, feature quality, and consistency between training and production.
• Build reusable tooling for model evaluation, retraining, and safe rollout.
• Own the quality and operation of machine learning components by writing tested production code, monitoring models and pipelines, investigating failures, and improving reliability, latency, and cost.
• Design and analyze online experiments with data science partners, connecting offline evaluation to product adoption and incremental impact while monitoring guardrails such as dismissals, unsubscribes, and user experience.
• Partner with product engineering to integrate models into recommendation delivery systems and with machine learning infrastructure teams to use and improve shared training, feature, and serving capabilities.
• Work with product, marketing, and sales partners to identify problems that shared machine learning capabilities can solve.

Requirements

• 3+ years of industry experience in machine learning engineering, software engineering, or applied data science, with hands-on experience building and shipping machine learning models in production.
• Strong programming skills in Python and experience writing maintainable, tested production code.
• Practical experience designing, training, and evaluating machine learning models using frameworks such as PyTorch, TensorFlow, XGBoost, or scikit-learn.
• Experience building data or feature pipelines, proficiency in SQL, and familiarity with distributed data processing tools such as Spark or PySpark.
• Strong understanding of statistics, model evaluation, and experimentation, including data leakage and distinguishing offline model improvements from business impact.
• Experience deploying, monitoring, and debugging production machine learning systems, and evaluating tradeoffs among model quality, reliability, latency, and cost.
• Ability to turn open-ended business problems into technical approaches and collaborate with engineering, data science, product, and business partners.

Preferred Qualifications

• Experience with recommendation systems, ranking, personalization, or marketplace and advertising optimization.
• Experience with contextual bandits, policy learning, causal inference, or off-policy evaluation.
• Experience building and evaluating LLM applications, including structured extraction, embeddings, or recommendations grounded in user and business context.
• Experience building reusable machine learning capabilities used by multiple products or teams, including training automation, feature systems, or model monitoring.
• Experience with product growth, lifecycle messaging, or systems that balance short-term engagement with longer-term user outcomes.