At a glanceSummarised by Seekless from the posting.
Must have7
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
Nice to have6
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
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
About the role
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
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Design, build, train, evaluate, deploy, and own ML models in production that detect fraud across Stripe’s global payments network
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Design and build large-scale ML systems that operate on diverse and large scale data
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Experiment and iterate on ML models to achieve key business goals around data quality and accuracy
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Develop pipelines and automated processes to train and evaluate models in offline and online environments
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Integrate ML models into production systems and ensure their scalability and reliability
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Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers
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Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions
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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