Senior Machine Learning Engineer - Cloud AI Platform/Infrastructure - job 16 of 20

As a Senior Machine Learning Engineer at Visa, your main responsibilities will include extending our AI as a Service platform to the Public Cloud, optimizing the existing codebase, and developing new technical solutions. You'll also be involved in ensuring the scalability, reliability, and interpretability of our AI systems, all while contributing to a governance framework that supports responsible AI practices.

To apply for the Senior Machine Learning Engineer position at Visa, you should have a solid background in AI and machine learning production systems, along with strong development skills specializing in public cloud technologies. A bachelor’s degree in Computer Science, Engineering, or a related field is typically required, but relevant experience and a deep understanding of cloud infrastructure will also be considered.

Visa's AI as a Service (AIaS) enhances business performance by providing powerful AI-driven insights that optimize decision-making processes. It combines advanced AI capabilities with strong governance to ensure that data, models, and applications are utilized effectively and reliably, helping businesses to automate tasks, predict trends, and adapt strategies based on data-driven analytics.

Working at Visa as a Senior Machine Learning Engineer offers an enriching environment where learning and growth are encouraged. You will be a key player in developing cutting-edge AI solutions within the innovative AIaS framework, collaborating with talented individuals in a hybrid model that promotes flexible working conditions while contributing to exciting projects that can have a global impact.

The Senior Machine Learning Engineer position at Visa is hybrid, which means you will have the flexibility to work both remotely and in the office. The exact number of days in the office will be determined by your hiring manager, allowing you to balance personal and professional commitments while contributing effectively to your team.

When answering this question, highlight specific cloud platforms you've used, such as AWS, Azure, or Google Cloud, and how you've applied machine learning solutions utilizing these technologies. Provide examples of projects where cloud resources enhanced model performance or streamlined workflows.

Discuss particular challenges such as data quality issues, versioning complexities, or performance bottlenecks. Be sure to mention how you addressed these challenges, focusing on your problem-solving skills and technical abilities.

Describe your methodologies for validating model performance, including cross-validation techniques, A/B testing, or monitoring post-deployment metrics. Explain the importance of continual learning and updating models based on new data.

Talk about specific techniques you've employed, such as hyperparameter tuning, feature engineering, or model selection strategies. Sharing quantitative results from previous optimizations can make your answer more compelling.

Discuss your understanding of best practices for data privacy and security, like data anonymization techniques and compliance with regulations such as GDPR. Highlight any experience you have with privacy-enhancing computation technologies.

Provide an example that demonstrates your teamwork skills, detailing how you communicated technical concepts to non-technical stakeholders and how collaboration influenced the project’s success. Emphasize the importance of diverse perspectives in problem-solving.

Share your familiarity with popular tools and frameworks such as TensorFlow, PyTorch, or scikit-learn. Discuss why you prefer them based on their features, ease of use, and your project needs.

By subscribing to relevant journals, attending conferences, and participating in online courses or webinars, you can show your proactive approach to professional development. Mention specific publications or thought leaders you follow.

Sharing experiences of guiding less experienced team members can illustrate your leadership abilities. Provide examples of how you’ve helped juniors grow, whether through code reviews, sharing knowledge in team meetings, or providing technical guidance.

Discuss your vision for how AI can revolutionize payment systems, such as increased efficiency through automation or enhanced security measures. Connect your focus areas with Visa's goals in the AI space to demonstrate alignment with the company.