Machine Learning Engineer

Stripe•South San Francisco, CA

8d•$212,000 - $318,000•Hybrid

About The Position

Stripe, LLC. 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.

Requirements

Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of experience in Building and shipping ML systems in production.
Two (2) years of experience in ML algorithms and model architectures.
Two (2) years of experience in Designing, training and evaluating machine learning models.
Two (2) years of experience in Productionizing and deploying machine learning models at scale.
Two (2) years of experience in Orchestrating data pipelines and leveraging large-scale datasets.
Two (2) years of experience in Building and deploying ML models to solve business problems.
One (1) year of experience in ML libraries and frameworks including PyTorch, TensorFlow, XGBoost or Spark.
One (1) year of experience in Deep learning, including transformers, test-time compute, or reinforcement learning.

Responsibilities

Design state-of-the-art ML models and large-scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints.
Design systems to speed up the time from idea to deployment of new models.
Experiment and iterate on ML models (using tools including 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.