Staff Machine Learning Engineer, Radar
Stripe · N/A · 8525 Radar - Eng · listed October 7, 2026
The shape of it
Seniority
Staff
Experience asked
10+ years
Where
Not stated
Requirements listed
7
Length
479 words
In the posting’s own words
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.
What it asks for · 7
- 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
Also a plus
- 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
What the job covers
- 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
Tools and skills named
Models & research
- Machine learning16×
- Deep learning2×
- LLM
- NLP
- PyTorch
- TensorFlow
Data
- Data pipelines
- Spark
- Statistics
Words the posting leans on
- models11×
- experience8×
- data6×
- fraud6×
- building4×
- product4×
- requirements4×
- systems4×
- build3×
- hands-on experience3×
- payments3×
- production3×
- radar3×
- scale3×
- 10+2×
- business2×
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