Machine Learning Engineer

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Machine Learning Engineer in USA

Stripe
Full time
Email

USA
08/18/2026
Report

South San Francisco, CA

Responsibilities

Design ML models and large-scale ML systems for underwriting and portfolio management subject to business, risk, regulatory, and engineering constraints.
Design systems that accelerate the development and deployment of new models.
Experiment and iterate on ML models using tools including PyTorch and TensorFlow.
Develop automated pipelines to train and evaluate models in offline and online environments.
Integrate ML models into production systems and ensure scalability and reliability.
Collaborate with product and strategy partners to propose, prioritize, and implement product features.
Evaluate emerging ML and AI developments and turn innovative ideas into production solutions.

Requirements

Bachelor’s degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field.
At least two years of experience building and shipping ML systems in production.
At least two years of experience with ML algorithms and model architectures, designing, training, and evaluating models, productionizing and deploying models at scale, orchestrating data pipelines and leveraging large-scale datasets, and solving business problems with deployed ML models.
At least one year of experience with ML libraries and frameworks including PyTorch, TensorFlow, XGBoost, or Spark.
At least one year of experience with deep learning, including transformers, test-time compute, or reinforcement learning.

Benefits

40 hours per week
50% telecommuting permitted
Multiple positions available
Equity, company bonus or applicable sales commissions/bonuses
401(k) plan
Medical, dental, and vision benefits
Wellness stipends

Salary: $212k - $318k/yr

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.

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