Machine Learning Engineer, Identity Product

Stripe; San Pablo; On-site; PHP 1,000,000 - 2,000,000; Full time

Job summary

A global financial infrastructure platform is seeking a Machine Learning Engineer to manage the lifecycle of applied ML model development for their identity product. The ideal candidate should have over 6 years of experience in production ML systems and be proficient with various ML libraries. This position offers the opportunity to work on transformative projects in a fast-paced environment focusing on solving fraud and identity challenges.

Qualifications

• 6+ years of industry experience building and shipping ML systems in production.
• Hands-on experience in designing, training, and evaluating machine learning models.
• Experience performing analysis and defining metrics for model performance.

Responsibilities

• Design and deploy new models to protect millions of users from fraud.
• Integrate new signals into ML pipelines and improve verification models.
• Collaborate with cross-functional teams to execute projects.

Skills

Machine Learning systems

ML libraries and frameworks

ML algorithms and model architectures

Education

MS/PhD degree in ML/AI or related field

Tools

Java

Ruby

Job description

Who we are: Stripe is a financial infrastructure platform for businesses. Millions of companies use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet.

Machine Learning at Stripe

Machine learning is an integral part of almost every service at Stripe. Key products and use-cases powered by ML at Stripe include merchant and transaction risk, payments optimization and personalization, identity verification, and merchant data analytics and insights.

Stripe handles over $1T in payments volume per year, which is roughly 1% of the world’s GDP. We process petabytes of financial data using our ML platform to build features, train models, and deploy them to production.

About the team

Before Stripe, every growing internet platform had a payments team. Today, every growing internet platform has an Identity team to verify trustworthiness of a user. Identity verification is a core piece of economic infrastructure for online businesses that enables businesses to digitally verify the user’s Identity for fraud, regulatory and trust & safety purposes.

Why Stripe Identity?

• Be part of a team that's building a product with the potential to transform how trust is established in the digital world
• Work on challenging technical problems at a global scale
• Contribute to a product that will serve diverse industries, from fintech to e-commerce and beyond
• Opportunity to shape the future of digital identity and fraud prevention

What you’ll do

We are looking for Machine Learning Engineers to own the end-to-end lifecycle of applied ML model development and deployment in service of consumer facing product Identity. You will work closely with software engineers, machine learning engineers, data scientists, and ML platform infrastructure teams to design, build, deploy, and operate Stripe’s ML-powered decisioning systems.

Responsibilities

• Design and deploy new models using tools and iteratively improve verification and fraud models to protect millions of users from fraud
• Integrate new signals into ML pipelines, derive new ML features, and build workflows to make this process fast
• Integrate new models and behaviors into Stripe’s core payment flow
• Propose and implement innovative product ideas to reduce costs and combat fraud at Stripe
• Collaborate and execute projects cross-functionally with the data science, product management, infrastructure, and risk teams

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role.

Minimum requirements

• 6+ years of industry experience building and shipping ML systems in production
• Proficient with ML libraries and frameworks
• Knowledge of various ML algorithms and model architectures
• Hands-on experience in designing, training, and evaluating machine learning models
• Experience performing analysis, including querying data, defining metrics, or slicing and dicing data to model performance and business metrics

Preferred qualifications

• MS/PhD degree in ML/AI or related field
• Experience with DNNs including the latest architectures such as transformers and LLMs
• Experience working in Java or Ruby codebases
• Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems
• Experience in adversarial domains such as Payments, Fraud, Trust, or Safety