Own the full lifecycle of ML models to detect fraud in Stripe's global payments network.
Posted by employer 6 hours ago
First seen on Joblaze 2 hours ago
Last verified on the company career page 2 hours ago
Skills & Technologies
What you'll build
- Design, build, train, evaluate, deploy, and own ML models in production
- Design and build large-scale ML systems
- Experiment and iterate on ML models
- Develop pipelines and automated processes
- Integrate ML models into production systems
Must have
- 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
Nice to have
- MS or PhD degree in ML/AI or a related field
- 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
- Proven track record of building and deploying ML systems
Requirements
- Experience
- 10+ years
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Staff Machine Learning Engineer will manage the entire lifecycle of machine learning models aimed at detecting fraud within Stripe's payment network. Key skills include proficiency with ML frameworks like PyTorch and TensorFlow, as well as experience in deploying large-scale ML systems. This position is ideal for a seasoned professional with over a decade of experience in ML, particularly in fintech or related fields, who can work autonomously and mentor others. The Radar team is focused on innovative solutions to combat evolving fraud tactics.
Joblaze insights
Quick facts
- How much experience is required?
- At least 10 years of relevant experience for this Staff Machine Learning Engineer, Radar role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: PyTorch, Spark, TensorFlow, XGBoost.
- What seniority level is this role?
- Stripe targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Staff Machine Learning Engineer, Radar role at Stripe.
From the original posting
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
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
- 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
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