Staff Machine Learning Engineer- AI Governance - job 17 of 21

As a Staff Machine Learning Engineer in AI Governance at Visa, your primary responsibilities include designing, developing, and maintaining scalable AI governance services. You will work closely with interdisciplinary teams to ensure AI systems adhere to ethical standards and regulatory requirements while applying robust architectural principles for effective solutions. Additionally, you will lead the development of advanced Responsible AI platforms utilizing the latest Generative AI technologies.

To qualify for the Staff Machine Learning Engineer role at Visa, candidates should possess extensive experience in machine learning, data science, or a related field, along with a strong understanding of AI governance. Familiarity with Generative AI technologies is essential, as well as experience working in cross-functional teams that engage with legal frameworks and best practices in AI ethics. A relevant degree in computer science or engineering is typically expected.

The AI Observatory product at Visa serves as an essential tool for maintaining an inventory of machine learning models and AI systems. It provides oversight for the complete lifecycle of models, ensuring their accuracy, transparency, fairness, and robustness. This product is a part of Visa’s broader mission to establish centralized AI excellence and enhance AI governance across the organization.

Visa promotes compliance in AI systems by having the Staff Machine Learning Engineer collaborate closely with data scientists, product managers, and legal experts to ensure that all AI solutions meet ethical standards and regulatory requirements. By applying strong architectural principles and integrating innovative technologies, you’ll contribute to building responsible AI platforms that prioritize compliance and trustworthiness.

At Visa, a Staff Machine Learning Engineer can look forward to a hybrid work environment that balances remote work with in-office collaboration. The role typically requires employees to work from the office at least three days a week, promoting teamwork and innovation while allowing flexibility to work from home, thus enhancing work-life balance.

When answering this question, focus on specific examples where you've worked with AI governance frameworks, emphasizing your understanding of ethical principles and compliance regulations. Highlight your role in developing or improving governance strategies and the impact these had on AI project outcomes.

Discuss principles such as modularity, scalability, and maintainability. Provide examples of how you've implemented these principles in past projects to develop robust AI solutions, including any specific technologies or frameworks you used.

Share your strategies for staying current, such as attending conferences, following industry publications, participating in forums, or taking online courses. Emphasize your commitment to continuous learning and how you apply new knowledge to your work.

Choose a scenario that illustrates a complex ethical dilemma related to AI. Detail the steps you took to address the issue, including collaboration with diverse stakeholders and integrating ethical considerations into the solution.

Emphasize your communication skills and proactive collaboration approach. Share specific strategies you employ to foster teamwork, such as regular check-ins, feedback loops, and creating a culture of openness and mutual respect.

Discuss the importance of making AI systems transparent and understandable for stakeholders. Explain how you ensure explainability in your development processes and the impact it has on trust and compliance.

Share a specific example of a technology you explored, the evaluation criteria you used, and how you integrated it into existing systems. Highlight the benefits achieved and the lessons learned from the experience.

Discuss your approach to prioritization, focusing on aligning projects with business goals, stakeholder needs, and compliance requirements. Emphasize your analytical skills and ability to make data-driven decisions.

Highlight various techniques like cross-validation, A/B testing, and monitoring metrics like accuracy and fairness. Discuss how you implement these techniques to ensure the models meet governance standards.

Share your insights on trends in AI development and governance, including the importance of ethical considerations, accountability, and technological advancements. Express your vision for how governance will evolve in response to new challenges in the industry.