Machine Learning Engineer, Supportability

at Stripe · Fintech · Canada · 8217 Risk Engineering

Stripe is seeking a Machine Learning Engineer for their Supportability Evaluation team. This role focuses on designing, building, training, evaluating, and deploying AI/ML models and large-scale systems for detection and decisioning within Stripe's financial ecosystem. The engineer will work on scaling an LLM-based system, integrating new capabilities through agentic approaches or supervised learning, and ensuring merchant compliance in real-time.

What you'd actually do

  1. 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
  2. Drive the expansion of Stripe's largest LLM-based system, scaling its usage and integrating new capabilities through agentic approaches or supervised learning.
  3. Rapidly prototype new AI/ML-based approaches to achieve key business goals.
  4. Develop processes to train and evaluate models in offline and online environments
  5. Integrate models into production systems and ensure their scalability and reliability

Skills

Required

  • 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.

Nice to have

  • 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

What the JD emphasized

  • AI/ML models and systems
  • detect and action supportability violations in real-time
  • building high-fidelity detection engines
  • ensure our merchants remain compliant across the globe
  • shipping AI/ML systems in production
  • productionizing and deploying models at scale
  • rigorously evaluating model performance
  • applying modern machine learning techniques and Generative AI models to solve complex business problems

Other signals

  • AI/ML models and systems
  • detection and actioning supportability violations
  • high-fidelity detection engines
  • merchant compliance
  • LLM-based system
  • agentic approaches
Read full 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

The Supportability Evaluation team acts as stewards of the financial ecosystem. Our mission is to protect Stripe’s reputation with our global financial partners by architecting highly precise, automated supportability controls. We develop the AI/ML models and systems that detect and action supportability violations in real-time. We're 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 world’s largest financial institutions.

What you’ll 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 Stripe’s 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 Stripe's 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