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Machine Learning Engineer, Supportability

Stripe

Salary not specified
Aug 18, 2025
San Francisco, CA, US • Seattle, WA, US
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Stripe is looking to solve business problems by leveraging machine learning and generative AI to improve existing products and create new ones. This includes enhancing risk detection, optimizing payments, personalizing user experiences, and improving internal operations through AI assistants.

Requirements

  • 2+ 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
  • Experience rigorously evaluating model performance, including cleaning data, and working with data-generating processes to improve signal and reduce noise in high-noise datasets.

Responsibilities

  • Design state-of-the-art ML models and large scale ML systems for detection and decisioning for Stripe products based on 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 ML-based approaches to achieve key business goals.
  • 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

Other

  • You are comfortable with ambiguity, love to take initiative, have a bias towards action, and thrive in a collaborative environment.
  • MS/PhD degree in ML/AI 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 ML systems that have effectively solved ambiguous business problems