Key skills
Machine learningPyTorchTensorFlowXGBoostSparkML model designML model trainingML model evaluationML model deploymentData pipeline orchestrationLarge-scale data processingDeep Learning architecturesTransformersReinforcement learningAdversarial domain experienceComfort with ambiguityAIMachine LearningMLDeep Learning
About this role
Stripe is a financial infrastructure platform for businesses, and they are seeking a Machine Learning Engineer for Stripe Capital. The role involves designing, building, and deploying machine learning models to provide financing opportunities while collaborating with various teams to enhance ML systems.
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 such as 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
Requirements:
- 5+ 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
- 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
- MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics)
- Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems
- Experience in adversarial domains such as Lending, Trading, Fraud
- Experience with Deep Learning including the latest architectures such as transformers, test-time compute, reinforcement learning