Machine Learning Engineer, Capital Underwriting på Stripe i US
Stripe · US
Machine Learning Engineer, Capital Underwriting
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 ...
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
Stripe Capital provides access to fast, flexible financing to small-and-medium businesses on Stripe to accelerate their growth,
and we lent over $1B in 2024. Businesses use the funds for marketing, team growth, geographic expansion, working capital, new
equipment purchases, and much more.
Machine learning is core to Stripe Capital’s business—we use information about businesses from their activity within and outside
of Stripe and our models to automatically underwrite uniquely tailored financing offers to their needs, which banks are often
unable to do. We are doing so through models with an established performance history, data infrastructure that is Stripe scale,
and a strong feedback loop that includes explainability, anomaly detection and a risk portfolio management layer. We're an
end-to-end team going from ideas to models to shipping in production.
WHAT YOU’LL DO
As a machine learning engineer for Stripe Capital, you'll be responsible for designing, building, training, evaluating, deploying,
and owning ML models in production with the goals of providing financing opportunities to as many users as possible while
satisfying financial performance goals. You'll work closely with software engineers, data scientists, product managers, and risk
managers to operate Stripe’s ML powered systems, features, and products. You'll also contribute to and influence ML architecture
at Stripe and be a part of a larger ML community.
RESPONSIBILITIES
Design state-of-the-art ML models and large scale ML systems for underwriting and portfolio management for Stripe Capital based
on ML principles, domain knowledge, risk, regulatory and engineering constraints
Design systems to speed up the time from idea to deployment of new models
Experiment and iterate on ML models (using tools such as PyTorch and TensorFlow) to achieve key business goals and drive
efficiency
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 and strategy partners to propose, prioritize, and implement new product features
Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized
solutions
WHO YOU ARE
We are looking for ML Engineers who are passionate about building ML systems that touch the lives of millions. You have experience
developing efficient feature pipelines, building advanced ML models, and deploying them to production. You are comfortable with
ambiguity, love to take initiative, have a bias towards action, and thrive in a collaborative environment.
We’re looking for someone who can bring new ideas to the table on building models able to push the state of the art at Stripe,
especially within the regulatory and operational constraints of a financing business.
MINIMUM REQUIREMENTS
5+ 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 complicated data pipelines and efficiently leveraging large-scale datasets
PREFERRED QUALIFICATIONS
* MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics)
Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems
Experience in adversarial domains such as Lending, Trading, Fraud
Experience with Deep Learning including the latest architectures such as transformers, test-time compute, reinforcement
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
Stripe processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from
startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an
integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting
opportunities to innovate in ML Platform at Stripe.
The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and
build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training
infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI
capabilities. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible,
and user-friendly systems that substantially increase ML velocity across the company.
WHAT YOU'LL DO
You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power
Stripe's ML-driven products. As a Staff Engineer, you'll make decisions with a large impact on Stripe. You'll influence our
investments and strategy while making our systems more reliable, secure, and a delight to use. You'll work cross-functionally with
other technical staff, data science, product, and senior leadership to increase the impact of ML at Stripe.
You'll help define the long-term strategy and lead the technical direction for the next generation of ML infrastructure that
powers Stripe's ML-driven products.
RESPONSIBILITIES
* Take ownership of end-to-end architecture and system design for large, complex projects across ML Platform.
* Define technical direction for highly ambiguous projects, transforming complex user needs into long-lasting platform strategy.
* Design system architectures for the most challenging ML Platform problems in one or more areas, including AI and ML workflow
orchestration, scalable CPU and GPU compute infrastructure, model training, LLM fine-tuning, low-latency model inference,
large-scale feature stores, real-time monitoring, and LLM and agent orchestration.
* Turn high-leverage ideas into tangible, robust solutions that shape platform and product roadmap, combining technical
excellence with creative problem-solving.
* Scope and lead large projects with significant business impact, driving them from requirements through design, implementation,
and production operation.
* Work with ML engineers, data scientists, and product teams directly to translate their needs into functional requirements and
scalable technical solutions.
* Arbitrate critical decisions that balance competing priorities while meeting latency, reliability, cost, and security
constraints.
* Serve as a key engineering representative, engaging senior leaders across Stripe and advising the leadership team on key
technical considerations related to the end-to-end ML lifecycle.
* Drive cross-team technical initiatives that improve ML development velocity and MLOps maturity across the company.
* Mentor and grow other engineers. Serve as a role model for designing, implementing, and operating great software systems.
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 professional software development experience, or equivalent domain expertise, with a solid background in
service-oriented architecture and large-scale distributed systems.
* Track record of serving as a technical lead, with the ability to provide technical direction, lead multi-team initiatives, and
mentor team members.
* Experience building and operating production ML platform in one or more areas such as model training, model serving,
orchestration, or ML data systems, with requirements for performance, reliability, scalability, and cost efficiency.
* Strong product instincts and a deep understanding of the business context in which you operate.
* Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical
stakeholders.
* Demonstrated ability to work cross-functionally, collaborating effectively with ML engineers, data scientists, software
engineers, product managers, and business stakeholders.
* The ability to thrive on a high level of autonomy and responsibility, and comfort operating in ambiguous environments.
* Hands-on experience using AI tools to accelerate how you work.
PREFERRED QUALIFICATIONS
* Experience building large-scale ML training, serving, or data infrastructure for machine learning use cases, such as
distributed training, model inference, feature stores, real-time feature computation, and model registries.
* Experience with distributed ML training systems, accelerator-backed compute, training data pipelines, experiment tracking, and
model evaluation.
* Experience rapidly developing prototypes and iterating based on user feedback.
* Experience training and shipping machine learning models to production to solve critical business problems.
* Familiarity with LLMs, LLM application frameworks, and agentic AI patterns (e.g., tool use, multi-agent orchestration,
retrieval-augmented generation).
* Familiarity with cloud services (e.g., AWS) and cloud-based AI and ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI).
* Ability to synthesize ideas across the organization while setting a compelling technical vision.
* Comfortable working with geographically distributed teams.
* Passion for side projects, open source, or self-driven technical initiatives.
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
Stripe processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from
startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an
integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting
opportunities to innovate in ML Platform at Stripe.
The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and
build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training
infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI
capabilities. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible,
and user-friendly systems that substantially increase ML velocity across the company.
WHAT YOU'LL DO
You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power
Stripe's ML-driven products. As a Staff Engineer, you'll make decisions with a large impact on Stripe. You'll influence our
investments and strategy while making our systems more reliable, secure, and a delight to use. You'll work cross-functionally with
other technical staff, data science, product, and senior leadership to increase the impact of ML at Stripe.
You'll help define the long-term strategy and lead the technical direction for the next generation of ML infrastructure that
powers Stripe's ML-driven products.
RESPONSIBILITIES
* Take ownership of end-to-end architecture and system design for large, complex projects across ML Platform.
* Define technical direction for highly ambiguous projects, transforming complex user needs into long-lasting platform strategy.
* Design system architectures for the most challenging ML Platform problems in one or more areas, including AI and ML workflow
orchestration, scalable CPU and GPU compute infrastructure, model training, LLM fine-tuning, low-latency model inference,
large-scale feature stores, real-time monitoring, and LLM and agent orchestration.
* Turn high-leverage ideas into tangible, robust solutions that shape platform and product roadmap, combining technical
excellence with creative problem-solving.
* Scope and lead large projects with significant business impact, driving them from requirements through design, implementation,
and production operation.
* Work with ML engineers, data scientists, and product teams directly to translate their needs into functional requirements and
scalable technical solutions.
* Arbitrate critical decisions that balance competing priorities while meeting latency, reliability, cost, and security
constraints.
* Serve as a key engineering representative, engaging senior leaders across Stripe and advising the leadership team on key
technical considerations related to the end-to-end ML lifecycle.
* Drive cross-team technical initiatives that improve ML development velocity and MLOps maturity across the company.
* Mentor and grow other engineers. Serve as a role model for designing, implementing, and operating great software systems.
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 professional software development experience, or equivalent domain expertise, with a solid background in
service-oriented architecture and large-scale distributed systems.
* Track record of serving as a technical lead, with the ability to provide technical direction, lead multi-team initiatives, and
mentor team members.
* Experience building and operating production ML platform in one or more areas such as model training, model serving,
orchestration, or ML data systems, with requirements for performance, reliability, scalability, and cost efficiency.
* Strong product instincts and a deep understanding of the business context in which you operate.
* Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical
stakeholders.
* Demonstrated ability to work cross-functionally, collaborating effectively with ML engineers, data scientists, software
engineers, product managers, and business stakeholders.
* The ability to thrive on a high level of autonomy and responsibility, and comfort operating in ambiguous environments.
* Hands-on experience using AI tools to accelerate how you work.
PREFERRED QUALIFICATIONS
* Experience building large-scale ML training, serving, or data infrastructure for machine learning use cases, such as
distributed training, model inference, feature stores, real-time feature computation, and model registries.
* Experience with distributed ML training systems, accelerator-backed compute, training data pipelines, experiment tracking, and
model evaluation.
* Experience rapidly developing prototypes and iterating based on user feedback.
* Experience training and shipping machine learning models to production to solve critical business problems.
* Familiarity with LLMs, LLM application frameworks, and agentic AI patterns (e.g., tool use, multi-agent orchestration,
retrieval-augmented generation).
* Familiarity with cloud services (e.g., AWS) and cloud-based AI and ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI).
* Ability to synthesize ideas across the organization while setting a compelling technical vision.
* Comfortable working with geographically distributed teams.
* Passion for side projects, open source, or self-driven technical initiatives.
Machine Learning Engineer
Location: Hybrid
Company: Ferritico
Employment type: Full-time
Ferritico is looking for a Machine Learning Engineer to design, develop, deploy, and continuously improve machine learning solutions for advanced materials and steel applications, with a strong focus on production-ready models, data workflows, cloud services, and product integration.
This is a hands-on technical role for someone who enjoys working at the intersection of machine learning, software engineering, data, and industrial product development.
About the role
You will contribute to the development of Ferritico's machine learning models and software platform. The role involves turning industrial and materials data into robust model logic, reliable validation workflows, scalable cloud services, and user-facing product features.
You will work closely with materials engineers, and customers to ensure that machine learning solutions are accurate, maintainable, well-documented, and aligned with real industrial needs.
Key responsibilities
Manage and organize the aggregation, cleaning, and preparation of materials, process, and property data in collaboration with materials engineers.
Design and develop machine learning models and appropriate model structures.
Define model assumptions, evaluation metrics, validation datasets, limitations, and acceptance criteria.
Validate, benchmark, and continuously improve existing and future machine learning models.
Develop and maintain cloud-based machine learning services, training workflows, and inference endpoints.
Monitor production models and troubleshoot performance, reliability, and data-quality issues.
Integrate new machine learning modules into Ferritico's web application.
Support customers in running simulations, understanding model outputs, and identifying suitable machine learning solutions for their processes.
Contribute to testing, technical documentation, code reviews, and engineering decision-making.
What we are looking for
We are looking for someone with a strong background in machine learning, data science, computer science, mathematics, engineering, artificial intelligence, or a related quantitative field.
The ideal candidate has:
An MSc, PhD, or equivalent practical experience in a quantitative field such as Computer Science, Mathematics, Engineering, Artificial Intelligence, or a related discipline.
Strong proficiency in Python and experience building clear, maintainable, and well-tested code.
Practical experience with pandas, scikit-learn, and common workflows for data preparation, model development, evaluation, and deployment.
Solid understanding of statistical modeling, machine learning methods, validation strategies, and performance metrics.
A basic understanding of backend and frontend development and how machine learning components integrate into software products.
Rigorous attention to detail, strong communication skills, and the ability to take ownership of high-quality deliverables in a collaborative team.
Nice to have
Experience with any of the following would be highly valuable:
Google Cloud Platform, cloud hosting, containerized services, or MLOps workflows.
Git-based version control, automated testing, continuous integration, and production monitoring.
Physics-informed machine learning, scientific computing, or models that incorporate domain constraints.
Materials engineering, metallurgy, steel-industry data, or other industrial engineering applications.
Customer-facing technical work, SaaS products, web applications, or translating business and process needs into machine learning solutions.
This role could be a strong fit if you
Have recently completed an MSc or PhD involving machine learning, statistical modeling, artificial intelligence, or scientific computing.
Have practical experience developing, validating, deploying, or maintaining machine learning models.
Enjoy combining data science with software engineering and practical product development.
Are an ambitious and independent learner who takes responsibility for results while collaborating closely with others.
Are excited about helping shape digital tools for the future of steel and advanced materials.
Why join Ferritico?
At Ferritico, you will join a Swedish software startup working at the frontier of materials science, AI, and industrial digitalization. Built on more than 10 years of research at KTH, our SaaS platform helps steel companies accelerate the development, manufacturing, and implementation of advanced alloys.
You will have significant responsibility and autonomy, work with a small multidisciplinary team, and influence both the machine learning foundation and product direction of a platform used in industrial production. We value teamwork, curiosity, technical excellence, and clear communication.
Not sure you meet every requirement?
We encourage you to apply even if your experience does not match every qualification listed above. We value diverse backgrounds, different perspectives, and people who are motivated to learn and contribute.
How to apply
Please send your CV and a short note describing your motivation for the role, along with your relevant experience in machine learning, data science, software engineering, or industrial applications, to: contact@ferritico.com (Please include “Machine Learning Engineer” in the email subject line)
Application deadline: 31 July 2026
