Machine Learning Engineer, Growth Platform

Fdetimes; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Stripe is seeking a machine learning engineer to design and deploy production ML systems that power product recommendations and personalized actions for businesses. You will work with data scientists, engineers, and product partners from problem definition through training, evaluation, deployment, monitoring, and iteration, turning model improvements into measurable outcomes.

We value practical tradeoffs, robust code, and collaboration across Stripe's Growth Platform to deliver scalable,

Qualifications

• 3+ years of industry experience in ML engineering, software engineering, or applied data science with hands-on production ML experience.
• Strong Python programming skills and ability to write maintainable, tested production code.
• Experience with ML frameworks such as PyTorch, TensorFlow, XGBoost, or scikit-learn.
• Data pipelines, SQL, and distributed processing with Spark/PySpark.
• Solid understanding of statistics, model evaluation, and experiments, awareness of data leakage.
• Experience deploying, monitoring, and debugging production ML systems; balance quality, latency, cost.
• Ability to turn business problems into technical approaches and collaborate with teams.

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 how recommendations adapt to context.
• Build agent-based recommendation capabilities using business context to identify relevant products with evaluations of usefulness.
• Develop data and feature pipelines for training and inference; improve data freshness and feature quality.
• Build reusable tooling for model evaluation, retraining, and safe rollout to accelerate improvements.
• Own the quality and operation of ML components: write production-grade code, monitor models, and troubleshoot.
• Design and analyze online experiments, linking offline evals to product adoption and user experience.
• Collaborate with product engineering to integrate models into delivery systems and with ML infra teams to improve training/serving.
• Work with product, marketing, and sales to identify problems where ML adds value.

Skills

Python programming

Production ML systems

Software engineering

ML model training and evaluation

SQL

Distributed data processing

Statistics and experimentation

Model deployment and monitoring

Cross-functional collaboration

Tools

PyTorch

TensorFlow

XGBoost

scikit-learn

Spark

PySpark

Job description

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

Growth Platform builds the machine learning systems that help businesses discover and use the Stripe products that meet their needs. Our recommendations reach users across the Dashboard, email, onboarding, documentation, and AI agent interfaces. We combine an understanding of each business with models that decide which recommendation is useful, when to show it, and how to learn from the outcome.

Our work spans recommendation and ranking models, contextual bandits, agent‑based recommendations, and the data and evaluation systems behind them. We build shared capabilities that product, marketing, and sales teams can use across Stripe. Success means helping businesses take useful actions and adopt products that help them grow, while keeping recommendations relevant and avoiding unnecessary messages.

What you’ll do

You will build and operate production ML systems that improve how Stripe recommends products, content, and next steps to businesses. You will own work from problem definition and feature development through training, evaluation, deployment, monitoring, and iteration. Working with data scientists, engineers, and product partners, you will turn model improvements into measurable user and business outcomes.

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 how recommendations adapt to user context and feedback.
• Build agent‑based recommendation capabilities that use business context to identify relevant products and integration options, with evaluations that test recommendation quality and usefulness.
• Develop reliable data and feature pipelines for training and inference. Improve data freshness, feature quality, and consistency between training and production.
• Build reusable tooling for model evaluation, retraining, and safe rollout so the team can test and ship improvements faster.
• Own the quality and operation of the team's ML components: write tested production code, monitor models and pipelines, investigate failures, and improve reliability, latency, and cost.
• Design and analyze online experiments with data science partners. Connect offline evaluation to product adoption and incremental impact, with guardrails for dismissals, unsubscribes, and user experience.
• Partner with product engineering to integrate models into recommendation delivery systems, and with ML infrastructure teams to use and improve Stripe's shared training, feature, and serving capabilities.
• Work with product, marketing, and sales partners to identify problems that shared ML capabilities can solve, and make practical choices about where modeling adds value.

Who you are

You are a machine learning engineer with a builder mindset. You care about the business problem, the quality of the model, and what happens after it ships. You can move between modeling and software engineering, make practical tradeoffs, and take ownership of an ambiguous problem through production and measurement.

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 3+ years of industry experience in machine learning engineering, software engineering, or applied data science, with hands‑on experience building and shipping ML models in production.
• Strong programming skills in Python and experience writing maintainable, tested production code.
• Practical experience designing, training, and evaluating ML 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.
• A strong understanding of statistics, model evaluation, and experimentation, including the ability to recognize data leakage and distinguish offline model improvements from business impact.
• Experience deploying, monitoring, and debugging production ML systems, and evaluating tradeoffs among model quality, reliability, latency, and cost.
• Ability to turn an open‑ended business problem into a technical approach and collaborate effectively 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 ML 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.