Own ML work across the full lifecycle to enhance fraud detection models for Stripe's payment network.
Posted by employer 5 hours ago
First seen on Joblaze 3 hours ago
Last verified on the company career page 3 hours ago
What you'll build
- Build, train, evaluate, and deploy ML models that detect fraud
- Research emerging fraud patterns and develop ML solutions
- Apply advances in deep learning to improve model quality
- Co-build new fraud and abuse products
Must have
- 2+ years of experience training, evaluating, and deploying ML models
- Proficiency in Python and common data and ML frameworks
- Strong knowledge of production ML systems
Nice to have
- Strong software engineering skills
- Experience building and optimizing real-time ML infrastructure
- Experience applying ML to fraud detection
Requirements
- Experience
- 2+ years
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Machine Learning Engineer will focus on developing and deploying models to detect fraud within Stripe's extensive payment network. Key skills include proficiency in Python and familiarity with frameworks like SQL, Spark, and PyTorch, alongside a solid understanding of production ML systems. This position is ideal for someone with at least two years of experience in machine learning, particularly in fraud detection or related fields. The Radar team is rapidly expanding its product offerings to combat emerging fraud patterns, making this a pivotal role in enhancing Stripe's security measures.
Joblaze insights
Quick facts
- How much experience is required?
- At least 2 years of relevant experience for this Machine Learning Engineer, Radar role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: PyTorch, Python, SQL, Spark.
- What seniority level is this role?
- Stripe targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Machine Learning Engineer, Radar role at Stripe.
From the original posting
Who we are
About Stripe
Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.
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 to protect against AI token theft, free trial abuse, and scripted 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
- Build, train, evaluate, and deploy ML models that detect fraud across Stripe’s global payments network
- Research emerging fraud patterns like token theft and develop ML solutions to address them
- Apply advances in deep learning to improve model quality and detection rates at scale
- Co-build new fraud and abuse products directly with top users
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
- 2+ years of experience training, evaluating, and deploying ML models in a production environment
- Proficiency in Python and common data and ML frameworks like SQL, Spark, and PyTorch
- Strong knowledge of production ML systems; and data analysis, statistics, and experiment design fundamentals
- Active interest in the latest ML developments, and how they can be leveraged to solve business problems
Preferred qualifications
- Strong software engineering skills and ability to design ML solutions through entire product stack
- Experience building and optimizing real-time, low-latency ML infrastructure at scale
- Experience applying ML to fraud detection, integrity, trust and safety, or a closely related domain
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