Machine Learning Engineer, Supportability

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companiesfrom the worlds largest enterprises to the most ambitious startupsuse 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 everyones reach while doing the most important work of your career.

About the team

The Supportability Evaluation team acts as stewards of the financial ecosystem. Our mission is to protect Stripes reputation with our global financial partners by architecting highly precise automated supportability develop the AI/ML models and systems that detect and action supportability violations in real-time. Were responsible for building high-fidelity detection engines that ensure our merchants remain compliant across the globe balancing the scale of millions of users with the surgical precision required by the worlds largest financial institutions.

What youll do

As a Machine Learning Engineer in Supportability you will be responsible for designing building training evaluating deploying and owning AI/ML models in production. You will work closely with software engineers machine learning engineers product managers and data scientists to operate Stripes ML powered systems features and products. You will also have the opportunity to contribute to and influence AI/ML architecture at Stripe and be a part of a larger community.

Responsibilities

• Design state-of-the-art AI/ML models and large scale systems for detection and decisioning for Stripe products based onAI/ML principles domain knowledge and engineering constraints
• Drive the expansion of Stripes largest LLM-based system scaling its usage and integrating new capabilities through agentic approaches or supervised learning.
• Rapidly prototype new AI/ML-based approaches to achieve key business goals.
• Develop processes to train and evaluate models in offline and online environments
• Integrate models into production systems and ensure their scalability and reliability
• Collaborate with product and strategy partners to propose prioritize and implement new product features
• Engage with the latest developments in AI/ML and take calculated risks in transforming innovative ideas into productionized solutions
• Explore cutting-edge AI/ML techniques and evaluate their potential to solve business problems

Who you are

We are looking for ML Engineers who are passionate about building AI/ML and AI systems that touch the lives of millions. You have experience building and evaluating advanced AI/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.

Minimum requirements

• 2 years of industry experience building and shipping AI/ML systems in production
• Proficient with AI/ML libraries and frameworks such as PyTorch TensorFlow XGBoost as well as Spark
• Knowledge of various AI/ML algorithms and model architectures
• Hands-on experience in designing training and evaluating machine learning models
• Hands-on experience in productionizing and deploying models at scale
• Experience rigorously evaluating model performance including cleaning data and working with data-generating processes to improve signal and reduce noise in high-noise datasets.
• Proficiency in creatively applying modern machine learning techniques and Generative AI models to solve complex business problems.

Preferred qualifications

• MS/PhD degree in AI/ML or related field (e.g. math physics statistics)
• Experience with DNNs including the latest architectures such as transformers and LLMs
• Experience working in Java or Ruby codebases
• Proven track record of building and deploying AI/ML systems that have effectively solved ambiguous business problems
• Experience with online experimentation such as A/B testing or multi-armed bandits.
• Experience with model calibration