stripe
Machine Learning Engineer
About this role
Stripe is seeking a Machine Learning Engineer to design and deploy advanced ML systems for underwriting and portfolio management within Stripe Capital. You'll build end-to-end ML pipelines, experiment with cutting-edge models, and collaborate with product teams to translate innovative ideas into production solutions.
What you'll do
- Design state-of-the-art ML models and large-scale systems for underwriting and portfolio management
- Build and optimize ML training and evaluation pipelines for offline and online environments
- Experiment with ML models using PyTorch and TensorFlow to achieve business objectives
- Productionize and integrate ML models into scalable, reliable production systems
- Streamline the model development lifecycle from concept to deployment
- Partner with product and strategy teams to develop and prioritize new features
What they're looking for
- Machine learning algorithms and model architectures
- Model training, evaluation, and optimization
- PyTorch and TensorFlow
- Production ML deployment and scaling
- Data pipeline orchestration and large-scale data handling
- Deep learning (transformers, reinforcement learning)
- XGBoost or Spark
- Business problem-solving with ML
Benefits
- Equity compensation
- Company bonus or sales commissions/bonuses
- 401(k) plan
- Medical, dental, and vision insurance
- Wellness stipends
- 50% remote work flexibility
Opens the official application on the employer’s site. No login required.
stripe
Stripe builds payment infrastructure and financial services platforms, offering APIs and tools that enable developers and businesses to process transactions, detect fraud, verify identity, and manage security at scale. The company is hiring Backend Engineers, Full Stack Engineers, ML Engineers, AI Engineers, and Security Engineers to develop core platform systems, payment intelligence, customer support infrastructure, and security data platforms.
- Website
- stripe.com
Likely interview questions
- Walk us through a production ML system you built end-to-end—what challenges did you face in moving from training to deployment?
- How do you approach experimenting with new model architectures while ensuring they meet business constraints and regulatory requirements?