Staff Machine Learning Engineer- AI Governance - job 20 of 21

As a Staff Machine Learning Engineer in AI Governance at Visa, you will play a crucial role in designing, developing, and maintaining scalable AI governance solutions. You'll apply strong architectural principles, collaborate with cross-functional teams, and work on creating responsible AI systems that comply with ethical standards and regulatory requirements. This includes oversight of AI model lifecycles and using Generative AI technology to enhance the Responsible AI platform.

To excel in the Staff Machine Learning Engineer position at Visa, you should have a strong background in machine learning, AI technologies, and software engineering. A degree in Computer Science, Artificial Intelligence, or a related field is beneficial. Experience with complex system architecture, legislative compliance, and interdisciplinary collaboration is vital to succeed in this role.

The Staff Machine Learning Engineer contributes significantly to Visa's mission in AI governance by developing advanced solutions that promote Trustworthy AI practices. Your work will ensure that AI systems are accurate, transparent, and fair, which is essential for maintaining consumer trust and regulatory compliance. Additionally, you will be involved in the oversight of AI models' lifecycles and the integration of innovative technologies.

The work environment for a Staff Machine Learning Engineer at Visa combines flexibility and collaboration. The role supports a hybrid working model, allowing you to balance remote work with in-office days. You'll be part of a dynamic team that engages in innovative projects, fostering a collaborative culture. Expect to work closely with various disciplines including data scientists and legal experts.

Valuable skills for a Staff Machine Learning Engineer in AI Governance at Visa include proficiency in machine learning and AI algorithms, software development, and system architecture. Additionally, strong communication skills, the ability to work with cross-functional teams, and expertise in the ethical implications of AI are essential. Familiarity with Generative AI technology will also be a significant advantage.

When discussing a complex machine learning project, focus on your specific role and contributions. Describe the problem, your approach, the technologies used, and the impact your work had on the team and end-users. Be sure to highlight collaboration with other disciplines, like data science or legal teams, if applicable.

To ensure compliance with ethical standards, I follow best practices for AI governance, such as regularly reviewing models for bias, transparency, and accountability. I collaborate with legal experts and adhere to regulatory guidelines, integrating compliance checks into the model development lifecycle.

I employ a combination of metrics such as accuracy, precision, recall, and F1-score, depending on the problem at hand. Additionally, I utilize cross-validation and test annotations to ensure models perform well on unseen data and align with business objectives.

I have hands-on experience with Generative AI frameworks, focusing on their application in developing innovative solutions. Highlight specific projects where you utilized these technologies and explain how they enhanced the product vision or improved existing technologies.

When working in interdisciplinary teams, I prioritize open communication and respect for each team member's expertise. I ensure clarity on goals and responsibilities while fostering a collaborative atmosphere to drive innovation and effectively address challenges.

Share a specific challenge related to AI governance, such as ensuring model fairness or handling regulatory changes. Discuss the steps you took to address the issue, emphasizing your problem-solving skills and proactive approach to continuous learning in the field.

Provide a detailed example where you identified a weakness in an AI system, the measures you took to enhance it—such as tuning algorithms, improving datasets, or changing model architectures—and the results of your efforts based on performance metrics.

Discuss the specific machine learning frameworks, programming languages (like Python or R), data visualization tools, and cloud platforms you've worked with. Highlight how you've applied these technologies to streamline workflows or improve AI governance practices.

I stay updated by following industry-leading publications, participating in webinars and conferences, and engaging in professional networks. I also engage in ongoing education and training to deepen my knowledge of evolving technologies and governance frameworks.

Transparency is critical in AI governance as it builds trust among users and stakeholders. I advocate for it by promoting practices that document model decisions, share error rates, and explain algorithmic choices while ensuring that these practices are part of the development lifecycle.