Staff Machine Learning Engineer, Radar

Who we are

About the team

The Radar ML team builds the fraud detection models that protect Stripe's $1.9 trillion payment network from fraud. The team owns 10+ real-time deep learning models that must constantly evolve to stay ahead of fraudsters. Each ML improvement translates directly into dollar impact for Stripe and its users.

The team's models also power the Radar product suite that tens of thousands of businesses use to screen payments and manage fraud. Radar is growing fast, and the team is actively building new products like defenses against AI token theft, free trial abuse, and programmatic attacks.

What you'll do

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. You will have opportunities to optimize Stripe’s most intensive ML models, and opportunities to ship 0-to-1 products from scratch.

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

Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum 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