Machine Learning Engineer

Visas Technology Organization 

Visas Technology Organization is a community of problem solvers and innovators reshaping the future of commerce through AIpowered payment technologies. We operate one of the worlds 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 youll 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 impactdriven Machine Learning Engineers who enjoy solving complex problems using a combination of software engineering data engineering and applied machine learning. 

As part of a crossfunctional product team you will design build deploy and operate ML solutions that directly support Visas core payment platforms and valueadded services. Your work will move beyond experimentation into realworld production systems that must meet strict requirements for reliability performance security and compliance. 

The Work Itself:

• Design build and operate productiongrade machine learning systems that run at Visas global scale for NLP and related workloads with strict latency and throughput targets.
• Develop endtoend 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 actorcritic 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 realworld production systems at Visa scale. The role focuses on contributing to ML pipelines model deployment and scalable services within a crossfunctional team with strong mentorship and handson learning. Prior professional experience is not required; success is driven by solid fundamentals curiosity and a willingness to grow into endtoend ML system ownership. 

Qualifications :

Basic Qualifications

• Bachelors 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 (MLfocused) 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 datadriven 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 crossfunctional teams. 
• Clear communication of technical concepts to both technical and nontechnical stakeholders. 

Adaptability & Learning:

• Openness to feedback and changing priorities. 
• Continuous drive to learn new ML data and cloud technologies. 

We dont 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 productiongrade 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.

Remote Work :

No

Employment Type :

Full-time