Machine Learning Engineer at Visa

Stockholm, , Sweden -

Full Time

Start Date

Immediate

Expiry Date

07 Jun, 26

Salary

0.0

Posted On

09 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Software Engineering, Data Engineering, Applied Machine Learning, MLOps, NLP, APIs, GPU Inference, RAG Pipelines, Kubernetes, Docker, Terraform, PyTorch, TensorFlow, CUDA, ONNX

Industry

IT Services and IT Consulting

Description

Company Description Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters — to you, to your community, and to the world. Progress starts with you. Job Description Visa’s Technology Organization Visa’s Technology Organization is a community of problem solvers and innovators reshaping the future of commerce through AI‑powered payment technologies. We operate one of the world’s most sophisticated processing networks, capable of handling tens of thousands of secure transactions per second across millions of merchants, financial institutions, and consumers worldwide. At Visa, you'll have the opportunity to create impact at scale tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters to you, to your community, and to the world. The Opportunity We are looking for talented, curious, and impact‑driven Machine Learning Engineers who enjoy solving complex problems using a combination of software engineering, data engineering, and applied machine learning. As part of a cross‑functional product team, you will design, build, deploy, and operate ML solutions that directly support Visa’s core payment platforms and value‑added services. Your work will move beyond experimentation into real‑world production systems that must meet strict requirements for reliability, performance, security, and compliance. The Work Itself: Design, build, and operate production‑grade machine learning systems that run at Visa’s global scale for NLP and related workloads with strict latency and throughput targets. Develop end‑to‑end ML pipelines covering data preparation, model training, validation, deployment, monitoring, and retraining. Build and maintain high-availability, fault-tolerant ML services and APIs, including load balancing and robust autoscaling for GPU inference. Design and implement advanced agentic AI systems: RAG pipelines, multi-step and branching agents, actor–critic control loops, validation/guardrail stages, and custom tools. Work closely with product, data, and platform teams to turn requirements into concrete ML system designs and production deployments across multiple Visa technology offerings. Continuously improve model quality, data quality, system reliability, and cost/performance of the ML stack. Essential Functions: Own ML model and service implementations end to end, from prototype to production. Apply MLOps practices for safe, repeatable deployment, monitoring, and lifecycle management of models and agents. Engineer scalable APIs and serving layers that integrate cleanly with existing systems and downstream applications. Use solid data structures, algorithms, and time/memory complexity analysis to make sound, scale-aware design choices. Participate in technical design reviews and architecture discussions, contributing an ML and systems perspective. Debug and optimize CPU/GPU inference, data pipelines, and distributed workloads in collaboration with other engineers. This role is designed for engineers who want to build strong foundations in software engineering and applied machine learning while working on real‑world, production systems at Visa scale. The role focuses on contributing to ML pipelines, model deployment, and scalable services within a cross‑functional team, with strong mentorship and hands‑on learning. Prior professional experience is not required; success is driven by solid fundamentals, curiosity, and a willingness to grow into end‑to‑end ML system ownership. Qualifications Basic Qualifications Bachelor’s degree in Computer Science Engineering Data Science or a related technical field or currently completing the degree Some practical experience as a Machine Learning Engineer, Software Engineer (ML‑focused), or Data Engineer with ML exposure. Strong foundation in programming preferably Python and core computer science concepts with academic or project exposure to machine learning data processing or software engineering Curiosity and willingness to learn applied machine learning ML operations and cloud based systems in a production environment Ability to collaborate effectively in cross functional teams communicate clearly and apply problem solving skills to real world engineering challenges Technical Expertise: Foundational Python programming skills, with some experience writing and maintaining production or production-adjacent code. Exposure to one or more system/server programming languages is a plus (e.g. C++, Go, Rust, or Java). Experience building and operating ML pipelines and models in production, preferably in NLP-focused problem spaces. Hands-on with PyTorch (Transformers, NN/MLP architectures),TensorFlow experience is a plus. GPU inference and optimization: CUDA, ONNX, experience with Triton Inference Server or similar serving stack is a strong plus. Kubernetes and Docker for deploying and operating ML services (namespaces, resource limits, rolling deploys). CI/CD for ML services and pipelines. Infrastructure as code with Terraform, including IAM and core data/compute resources. Experience with agentic AI frameworks and patterns (e.g. Google ADK, custom toolchains, RAG orchestration). Experience with Kubeflow Pipelines (KFP) or similar systems for model training workflows. Experience with at least one major cloud platform for ML (AWS, GCP, or Azure), e.g. GCP Vertex AI or AWS SageMaker. Growth & Innovation Mindset: Curiosity and passion for machine learning and data‑driven systems. Comfort challenging existing solutions and learning new tools, frameworks, and platforms. Interest in areas such as MLOps, model monitoring, feature engineering, and responsible AI. Problem Solving & Collaboration: Ability to approach complex problems logically and creatively. Strong collaboration skills and willingness to learn from peers and mentors in cross‑functional teams. Clear communication of technical concepts to both technical and non‑technical stakeholders. Adaptability & Learning: Openness to feedback and changing priorities. Continuous drive to learn new ML, data, and cloud technologies. We don’t expect you to have experience with every tool or technique listed. Instead, we look for engineers with strong fundamentals, curiosity, and the ability to grow into building and owning production‑grade machine learning systems at scale. Additional Information Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law. Job Family Group: Technology and Operations

Responsibilities

The role involves designing, building, and operating production-grade machine learning systems for NLP and related workloads at global scale, focusing on developing end-to-end ML pipelines from data preparation to deployment and monitoring. Engineers will also build high-availability ML services, implement agentic AI systems like RAG pipelines, and collaborate with cross-functional teams to deploy ML solutions into core payment platforms.