Full Time On-site

Machine Learning Engineer, Capital Underwriting

at Stripe External
5+ yrs San Francisco 2 months ago
Apply on Stripe Sourced from Greenhouse
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Job Overview
Job Type
Full Time
Work Mode
On-site
Job Description

Stripe is a financial infrastructure platform empowering businesses globally. Millions of companies, from large enterprises to ambitious startups, rely on Stripe to process payments, boost revenue, and unlock new business opportunities. Our mission is to increase the GDP of the internet, and we are seeking talented individuals to help us achieve this ambitious goal.

Machine learning is fundamental to Stripe's operations, powering critical services such as merchant and transaction risk assessment, payment optimization, identity verification, and data analytics. We leverage cutting-edge generative AI technologies to enhance product experiences and develop AI Assistants for both our customers and internal teams, aiming to improve productivity across various departments.

The Stripe Capital team provides fast, flexible financing to small and medium-sized businesses on Stripe, enabling their growth. Our machine learning models are central to this offering, automatically underwriting tailored financing offers based on business activity and external data, a service often unavailable through traditional banking channels. We are an end-to-end team, responsible for everything from ideation to model deployment in production.

Responsibilities
  • Design state-of-the-art ML models and large-scale ML systems for underwriting and portfolio management for Stripe Capital, considering ML principles, domain knowledge, risk, regulatory, and engineering constraints.
  • Design systems to accelerate the time from idea to deployment of new models.
  • Experiment and iterate on ML models (using tools such as PyTorch and TensorFlow) to achieve key business goals and drive efficiency.
  • Develop pipelines and automated processes to train and evaluate models in offline and online environments.
  • Integrate ML 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 ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions.
Skills & Expertise
Apache Spark
Data Pipelines
Deep Learning
Financial Services
LLMs
Machine Learning
Model Deployment
Production Systems
PyTorch
Risk Management
TensorFlow
Transformers
XGBoost
Requirements
  • 5+ years of industry experience building and shipping ML systems in production.
  • Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark.
  • Knowledge of various 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.
  • Hands-on experience in orchestrating complicated data pipelines and efficiently leveraging large-scale datasets.
  • Hands-on experience in collaborating across multiple teams, especially Data Science and Risk Management teams.
  • MS/PhD degree in ML/AI or related field (e.g., math, physics, statistics) is preferred.
  • Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems is preferred.
  • Experience in adversarial domains such as Lending, Trading, Fraud is preferred.
  • Experience with Deep Learning including the latest architectures such as transformers, test-time compute, reinforcement learning is preferred.