Machine Learning Engineer

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 Specialist Solutions Architects (SSA) are domain experts aligned to specific Stripe solutions and customer buying centers. We have in-depth knowledge of specific solution offerings and understanding of customers' challenges. We advise the broader pre-sales team on complex opportunities, support pipeline development and revenue attainment, serve as an advisor on go-to-market sales plays, and provide critical insights to our product and engineering teams. Stripe Radar is Stripe's fully integrated fraud and risk management platform. The Radar team's mission is to evolve from a best-in-class payment fraud product into a unified risk intelligence platform that protects businesses from all forms of financial loss—from sophisticated payment fraud and account takeovers to first-party misuse and policy abuse. With Radar, businesses can confidently navigate the evolving fraud landscape, accept more good revenue, and focus on growth. WHAT YOU'LL DO You'll collaborate closely with sales and pre-sales teams to drive pipeline and revenue targets, while also educating the product and engineering teams on market trends and influencing the product roadmap. You'll develop deep relationships with senior customer business leaders and grow Stripe's solution offerings with new and existing customers. As subject matter experts, you'll scale the broader organization by creating reusable assets and coaching others on complex high-value opportunities. You'll also run customer workshops and take the lead on key opportunities. You'll serve as the domain and product expert to our users and GTM team for Stripe Radar globally. RESPONSIBILITIES • Act as a subject matter expert on Stripe's Radar product suite to accelerate opportunities globally. • Demonstrate a deep understanding and point of view on trends in the fraud and risk management ecosystem, including AI-powered detection, processor-agnostic architectures, and the shift from payment fraud to holistic business risk (e.g., policy abuse, account takeover, first-party misuse). • Develop expertise with the functional and architectural patterns for deploying advanced fraud systems, including the lifecycle of rule management, ML model tuning, and manual review optimization. • Understand the ecosystem of risk and identity integrations (e.g., device fingerprinting, identity verification, bot mitigation, case management) Stripe must support for our users to be successful. • Define, share, and learn best practices and reusable assets with the broader GTM organization to enhance the quality and efficiency of the team. • Create reference architectures to uplevel the team and refine reference solutions based on product enhancements and feedback from customers. • Partner closely with the Radar product sales team and be an active part of the opportunity team for Stripe's largest and most complex Radar customers to lead strategy and own key customer relationships. • Partner closely with the Radar product team to get early exposure to the new products and features. • Serve as a "Voice of the Customer" internally to drive product and feature request prioritization, providing actionable insights on the competitive landscape and emerging fraud vectors. • Act as an escalation point to drive our most complex technical issues to resolution with Stripe's engineering team. • Support customers and Stripe teammates on account or deal strategy, and answer product feature and technical questions. • Understand the sales process for the product and provide the Sales team with tools and training to qualify potential leads. • Stay current on competitive analysis and market differentiation. • Support marketing events including executive briefings, conferences, user groups, and trade shows. MINIMUM REQUIREMENTS • 7+ years of experience in a pre-sales, technical consulting, product, or risk strategy role focused on e-commerce fraud prevention, risk management, or trust and safety • Deep working knowledge of the fraud and risk ecosystem, including payment fraud (CNP), account takeover (ATO), first-party misuse (friendly fraud), and policy and content abuse • Demonstrated knowledge of risk management architectures, including the role of rules engines, machine learning models, and manual review queues in fraud decisioning • Strong knowledge of the signals and data used in fraud detection, such as device and IP data, behavioral biometrics, identity verification, and graph-based analysis • Strong knowledge of software engineering and architecture patterns, with the ability to understand how a wide variety of technologies and systems interact with each other • Outstanding communication, presentation, and interpersonal skills, comfortable explaining complex concepts to both technical and non-technical audiences • Interest in solving open-ended business problems with a combination of technology and creative thinking • A proven ability to build strong collaborative working relationships with business partners • Ability to deal effectively with ambiguity and thrive in an unstructured, fast-moving environment PREFERRED QUALIFICATIONS • Experience integrating Stripe or other RESTful APIs into web applications • Experience with processor-agnostic fraud solutions and multi-processor payment environments • Experience with risk management for various business models, such as platforms and marketplaces, subscription services, and digital goods and SaaS • Familiarity with liability shift programs (e.g., chargeback guarantees) and value-based pricing models for risk products • Understanding of how to extend fraud protection beyond payments to other products like payouts (Issuing), physical payments (Terminal), or recurring billing (Billing) • Experience as a subject matter expert in a relevant industry, vertical, or product

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