Machine Learning Engineer

Stripe

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.

Ferritico AB

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

Adyen

This is Adyen Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition.  For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. Machine Learning Engineer Adyen is hiring a Machine Learning Engineer for the Payments Core Data team in Bengaluru, our newest office. You will be responsible for designing, productionizing, and maintaining robust, scalable machine learning services that power data products at Adyen. In this role, you will:  * Build the Platform Bridge: Develop and maintain full-lifecycle production ML pipelines while acting as a technical anchor to adapt global frameworks for (India) localized requirements, ensuring architectural parity and high-performance execution. * Drive Applied Decision Science: Design and productionize end-to-end recommendation and classification models integrated directly into merchant-facing products, turning complex data patterns into real-time, actionable insights. * Optimize for Performance: Identify and resolve performance bottlenecks in training and inference (memory, latency, throughput) to ensure ML solutions scale seamlessly within a high-throughput production environment. * Architect the Intelligence Layer: Own the development of reusable AI components - that serve as the foundation for scaling Generative AI applications. * Champion Technical Excellence: Promote and apply software and data engineering best practices while partnering with international MLOps and platform departments to seamlessly adopt internal toolsets. Who You Are: * Experienced Engineer & Python Expert: You have 4+ years of experience in the machine learning domain with expert-level Python skills and deep familiarity with the standard data science toolkit (e.g., PyTorch, TensorFlow, XGBoost/LightGBM, Pandas, and Scikit-learn). * Full-Lifecycle Architect: You are proficient in the end-to-end ML lifecycle - from leveraging big data (Spark, SQL/Trino) to build robust pipelines to deploying and maintaining models in production using MLOps best practices. * Infrastructure & Automation Savvy: You have hands-on experience with ML infrastructure and orchestration tools such as Kubernetes, Docker, Airflow, Argo-Workflows, and monitoring stacks like Prometheus and Grafana. * Experimental & Iterative Mindset: You thrive in a "launch fast and iterate" environment, applying strong foundational knowledge of statistics and ML techniques to solve complex real-world problems. * Technical Leader & Communicator: You proactively take ownership of projects from ideation to deployment, with the ability to lead stakeholders and translate complex technical outcomes into clear insights for any audience. Nice to Have: * You have experience with distributed GPU compute environments * You have experience working with a Machine Learning ‘Feature Store’ Data positions at Adyen: We know companies handle different definitions for their data-related positions, this is for instance dependent on the size of a company. We categorized and defined all our positions. Have a look at this blogpost to find out!  Our Diversity, Equity and Inclusion commitments  Our unique approach is a product of our diverse perspectives. This diversity of backgrounds and cultures is essential in helping us maintain our momentum. Our business and technical challenges are unique, and we need as many different voices as possible to join us in solving them - voices like yours. No matter who you are or where you’re from, we welcome you to be your true self at Adyen.  Studies show that women and members of underrepresented communities apply for jobs only if they meet 100% of the qualifications. Does this sound like you? If so, Adyen encourages you to reconsider and apply. We look forward to your application! What’s next? Ensuring a smooth and enjoyable candidate experience is critical for us. We aim to get back to you regarding your application within 5 business days. Our interview process tends to take about 4 weeks to complete, but may fluctuate depending on the role. Learn more about our hiring process here. Don’t be afraid to let us know if you need more flexibility. This role is based out of our Bengaluru office. We are an office-first company and value in-person collaboration; we do not offer remote-only roles.