Machine Learning Engineer, Growth Platform
Stripe Open · verified Oct 22, 2026 At a glance
Mid-level Machine Learning Engineering role at Stripe. San Francisco · no salary stated · we estimate $197,625–$279,000.
Stripe has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and run production models that select products, content and next steps for businesses across Growth Platform surfaces
- Carry model work from defining the problem and building features through deployment, monitoring and iteration
- Improve bandit and policy-learning methods by refining exploration, rewards and responses to user context
- Create agent-based recommendations using business context, then test whether they are relevant and useful
- Develop dependable data pipelines and reusable evaluation, retraining and rollout tools for ML systems
What you bring
- At least three years in ML engineering, software engineering or applied data science, including hands-on production model delivery
- You program in Python and build maintainable, tested production software
- You have trained and assessed models with a framework such as PyTorch, TensorFlow or another listed option
- You have built data or feature pipelines, use SQL, and understand statistical evaluation, experimentation and production ML operations
Who this fits
This mid-level role suits an engineer who can move between modeling and software delivery, take an open-ended business problem into production, and measure its results. You’ll work with engineering, data science, product, marketing and sales partners. The role is based in San Francisco.
From the employer
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.