Stripe

Stripe

Machine Learning Engineer, Radar

Seattle · Mid

Sponsorship not specifiedDetected 10 hours ago
Machine LearningDeep LearningData AnalysisData ScienceNLPLLMsMLOpsStatisticsSensors

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odds of building a lasting career here

42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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Employer immigration record

from this employer's Department of Labor filings

Green-card filing pattern in this occupation

Context, not a finding about this posting: of this employer's 34 green-card filings in this occupation, 100% were for a worker who already held the job.

Green-card follow-through: 72%

Of 47 labor certifications old enough to have been used, 13 expired without the employer filing the next step. Median time from filing to decision: 512 days.100% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.

Files H-1B transfers

70 transfer filings in the last year, covering 70 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • Our models make real-time decisions across a significant share of internet commerce - helping businesses stop fraud, increase authorization, and grow safely.
  • The team is growing, and expanding into multiple new areas.

Responsibilities

  • You will own ML problems from conception to production: analyzing new fraud and abuse patterns, exploring large-scale datasets, experimenting with model architectures, designing evaluations, deploying models, and monitoring performance.
  • Your work may include developing transformer architectures for payment data, building graph-powered intelligence across consumers, leveraging LLMs to detect emerging attacks, or rapidly prototyping new protections with frontier AI labs.
  • Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe's global network.
  • Investigate emerging fraud and abuse patterns, identify where ML can help, and rapidly develop effective solutions.
  • Design features, datasets, evaluation methods, and experiments for noisy, delayed, and adversarial environments.
  • Partner with engineering, product, data science, risk experts, and leading Stripe users to turn modeling improvements into differentiated Radar products.
  • Contribute to reusable tools and AI-assisted workflows that help the team develop and operate ML systems more effectively.
  • You will own ML problems from conception to production:
  • Experience building or operating production ML systems, including one or more of large-scale data and feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring.
  • Ability to communicate technical ideas and results clearly and collaborate effectively across disciplines.

Requirements

  • You are a fast, pragmatic problem solver with deep ML expertise and a bias toward action.
  • Strong programming and software-engineering skills, with experience writing tested, maintainable code for production ML applications.
  • Ability to investigate ambiguous problems, reason from data, and choose pragmatic technical approaches.
  • 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.
  • We're looking for someone who meets the minimum requirements to be considered for the role.

Nice to have

  • Experience owning an ML problem across the full lifecycle, from problem framing and data exploration through deployment, monitoring, and iteration.
  • Experience applying ML to fraud detection, integrity, trust and safety, identity, cybersecurity, or another adversarial domain.
  • Active interest in the latest ML developments, and how they can be leveraged to solve business problems.

Benefits

  • Improve model architectures, training methods, and production systems to increase model quality, coverage, and adaptability.
  • You are excited to work at the frontier of payments and AI - turning advances in machine learning into production systems that address new forms of fraud and abuse.
  • The preferred qualifications are a bonus, not a requirement.
  • 2+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
  • Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design.

Company info

  • 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 machine learning systems that power Stripe's Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce - helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for 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.
  • The Radar ML team builds the machine learning systems that power Stripe's Radar fraud and abuse product suite.
  • By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe.
  • We are building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for 50+ global payment methods.
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This listing is sourced directly from Stripe's careers page and normalized into a canonical job model.