Staff Machine Learning Engineer- AI Governance - job 13 of 21

As a Staff Machine Learning Engineer in AI Governance at Visa, your primary responsibilities will include designing, developing, and maintaining scalable AI governance services. You'll work closely with interdisciplinary teams to ensure AI systems comply with ethical standards and regulatory requirements while addressing evolving challenges in AI governance.

To qualify for the Staff Machine Learning Engineer- AI Governance role at Visa, candidates typically need a strong background in machine learning, AI technologies, and software engineering principles. Relevant experience in data science, along with knowledge of ethical AI practices and regulatory compliance, is also crucial for success in this role.

The AI Observatory product is pivotal for AI governance at Visa, providing an inventory of machine learning models and AI systems. It ensures oversight throughout the models' life cycles and governs aspects like accuracy, fairness, and transparency, ultimately supporting Visa's commitment to responsible AI usage.

The Staff Machine Learning Engineer at Visa will work predominantly with Generative AI technology. You’ll design and develop advanced Responsible AI platforms, which will incorporate the latest technologies to solve complex challenges in AI governance.

Candidates for the Staff Machine Learning Engineer role at Visa can expect a hybrid work model that allows them to alternate between remote work and the office. Typically, employees are expected to work in-office three days a week, promoting collaboration while also offering the flexibility of remote work.

In answering this question, highlight specific governance frameworks you have worked with, explaining how they ensure responsible AI use. Discuss any implementations you’ve been involved in that emphasize ethical standards and regulatory compliance.

To answer effectively, share your approach to applying robust architectural principles. Emphasize your experience with designing scalable architectures and maintaining system reliability through various testing and auditing methods.

When addressing this question, talk about specific methodologies you use for performance assessment, such as A/B testing, cross-validation techniques, or metrics like precision and recall, and how they guide improvements in AI governance.

Share a relevant project where you faced significant challenges, detailing the nature of these challenges and the specific strategies you implemented to overcome them. Highlight teamwork, innovation, and adaptability.

In your response, mention specific tools or platforms you've found effective in AI governance, and explain how they help maintain compliance and support ethical AI practices, focusing on your first-hand experience using these technologies.

Discuss your strategies for staying updated, such as following industry journals, attending conferences, participating in online courses, or engaging with professional networks dedicated to AI and machine learning topics.

Illustrate your collaborative experience by describing previous interdisciplinary projects. Discuss how you communicate effectively, align goals with different teams, and adapt your approach based on the diverse backgrounds of team members.

Detail the ethical considerations that matter most to you, such as fairness, transparency, and accountability. Provide examples of how you have applied these principles in your AI projects and how they influence your decision-making.

Explain the steps you would take to identify, mitigate, and eliminate bias in AI models. Discuss tools for bias detection, strategies for model tuning, and techniques for ensuring fairness in AI outcomes.

When discussing your experience, provide specific examples of how you've applied Generative AI in real-world situations, including any challenges faced, solutions implemented, and the impact achieved within your projects.