At a glance
- Experience
- 10+ years experience
- Location
- N/A · US
About the role
- The Radar ML team builds the fraud detection models that protect Stripe's $1.9 trillion payment network from fraud.
- In this role, you will own ML work across the full lifecycle: researching new fraud patterns, building and deploying models, and sharing results directly with top Stripe customers.
Responsibilities
- Design, build, train, evaluate, deploy, and own ML models in production that detect fraud across Stripe’s global payments network
- Design and build large-scale ML systems that operate on diverse and large scale data
- Experiment and iterate on ML models to achieve key business goals around data quality and accuracy
- 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, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers
- Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions
- Mentor engineers and contribute to a strong ML engineering culture within the team
Requirements
- 10+ 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 data pipelines and efficiently leveraging large-scale datasets
- Strong collaboration skills and the ability to work across teams and contribute to peers' success
- Ability to thrive with a high level of autonomy and responsibility and an entrepreneurial mindset
Preferred qualifications
- MS or PhD degree in ML/AI or a related field (e.g., math, physics, statistics, computer science)
- Experience in fintech, open banking, or financial data domains
- Experience with NLP, LLMs, or text classification at scale
- Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality
- Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems
- Experience with deep learning architectures, including transformers
Skills
- PyTorch
- TensorFlow
- XGBoost
- Spark
- NLP
- LLMs
- Deep Learning
- Transformers
