Machine Learning Engineer – Credit Risk
| Company | Stripe |
|---|---|
| Location | Toronto, ON, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior, Expert or higher |
Requirements
- 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
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
- Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, and XGBoost) 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
Preferred Qualifications
- 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
- Experience in adversarial domains such as Payments, Fraud, Trust, or Safety