> Markdown version of [/jobs/ext/3966470-machine-learning-engineer-growth-platform](https://www.wearedevelopers.com/jobs/ext/3966470-machine-learning-engineer-growth-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer, Growth Platform

- **Company:** Stripe, Inc.
- **Location:** Chicago, IL, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Programming, Information Leak Prevention, Software Debugging, Distributed Computing Environment, Monitoring of Systems, Information Extraction, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Software Engineering, SQL Databases, Pytorch, Large Language Models, Apache Spark, Model Validation, Pyspark, Scikit Learn, Production Code, Xgboost, Machine Learning Operations
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-engineer-growth-platform-chicago-il--867f04c1-392d-4609-b2da-89aeff826a2e

## About the Role

Machine Learning, Startup, Revenue Growth, Machine Learning, Reporting Dashboards, Onboarding, Documentation, Artificial Intelligence (AI), Business Model, Sales, * 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., * 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.

## Description

* 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 teams 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 Stripes 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.