Machine Learning Engineer, Credit Risk
Description For Machine Learning Engineer, Credit Risk
Stripe, a leading financial infrastructure platform, is seeking a Machine Learning Engineer to join their Credit Risk team. This role is crucial in building and deploying ML systems that protect Stripe's financial position while maintaining an excellent user experience.
The position sits within Stripe's Credit Detection team, where ML is integral to managing credit risk at scale. You'll be working with petabytes of financial data, handling approximately 1% of the world's GDP in payment volume. The role involves using both traditional ML models (like logistic regression and random forests) and cutting-edge deep learning approaches including transformers and LLMs.
As a Machine Learning Engineer, you'll be responsible for the full ML lifecycle - from design and development to deployment and maintenance of production systems. You'll collaborate with cross-functional teams including software engineers, product managers, and data scientists to build and operate Stripe's ML-powered systems.
The ideal candidate brings 6+ years of industry experience in building and deploying ML systems at scale. You should be proficient with major ML frameworks (PyTorch, TensorFlow, XGBoost) and comfortable with large-scale data processing using tools like Spark. Experience with deep neural networks, particularly modern architectures like transformers and LLMs, is highly valued.
Working at Stripe means joining a company that processes over $1T in payments annually and is at the forefront of financial technology innovation. You'll have the opportunity to work on challenging problems with significant business impact, while contributing to Stripe's mission of increasing the GDP of the internet.
The role offers competitive compensation (C$193,500 - C$290,300 annually) along with comprehensive benefits including equity, bonuses, retirement plans, and health coverage. The position follows Stripe's hybrid work model, requiring at least 50% of time spent in the Toronto office, balancing collaborative in-person work with flexibility.
Join Stripe if you're passionate about building ML systems that impact millions of users, thrive in collaborative environments, and want to be at the forefront of applying AI to financial technology.
Responsibilities For Machine Learning Engineer, Credit Risk
- Design state-of-the-art ML models and large scale ML systems for detection and decisioning
- Experiment and iterate on ML models to achieve key business goals and drive efficiency
- Develop pipelines and automated processes to train and evaluate models
- 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 transform innovative ML ideas into productionized solutions
Requirements For Machine Learning Engineer, Credit Risk
- 6+ 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
Benefits For Machine Learning Engineer, Credit Risk
- Equity
- Company bonus
- Retirement plans
- Health benefits
- Wellness stipends
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