Senior Machine Learning Engineer-Visa AI as a Service - job 19 of 20

As a Senior Machine Learning Engineer at Visa AI as a Service, you will be responsible for developing and implementing high-quality AI solutions that align with business requirements. Your duties will include collaborating with various team members, managing risks and issues, and driving end-to-end development efforts. You will also be involved in hands-on design, documentation, coding, testing, and debugging to ensure the performance and maintainability of existing functionalities.

To be considered for the Senior Machine Learning Engineer role at Visa, you should possess solid development experience combined with strong technical leadership and problem-solving skills. A comprehensive understanding of both web application and web service development is essential, along with proven experience in Agile methodologies. Having knowledge of data-driven solutions and AI governance will also be highly beneficial.

This is a hybrid position at Visa AI as a Service, meaning that while some work can be done remotely, there will be specific days when you are expected to work from the office in Foster City. The exact number of office days will be confirmed by your Hiring Manager upon hiring.

Essential skills for a Senior Machine Learning Engineer at Visa include expertise in machine learning algorithms, coding proficiency in relevant programming languages, as well as experience with cloud-native platforms and AI governance. Additionally, strong communication skills are crucial as you'll be presenting technical concepts to both technical teams and business stakeholders.

As a Senior Machine Learning Engineer at Visa, you will engage in a variety of projects focused on developing and optimizing AI-driven solutions that directly support Visa's business objectives. These projects will involve creating new data-driven functionalities, improving existing services for better performance, and ensuring that all solutions adhere to compliance and architectural standards.

When discussing a challenging machine learning project, focus on the problem you faced, the approach you took, and the outcomes you achieved. Highlight any unexpected challenges and how you addressed them, emphasizing your problem-solving skills and technical expertise.

Discuss your approach to prioritizing tasks, such as using agile methodologies or prioritization frameworks to assess urgency and importance. Provide examples of how you have successfully managed multiple projects while ensuring timely delivery and quality outcomes.

Your answer should detail your systematic approach to identifying issues, such as analyzing data quality, model performance metrics, and testing hypotheses. Share a specific instance where your debugging led to significant improvements in model reliability.

Outline the feature selection techniques you are familiar with, such as correlation analysis, recursive feature elimination, or regularization methods. Be prepared to discuss your rationale for choosing certain methods based on project requirements.

Talk about your experience collaborating with cross-functional teams to gather requirements and feedback. Explain how you incorporate business objectives into the development process to ensure that the final solution meets stakeholders' expectations.

Discuss your familiarity with cloud services relevant to AI and machine learning, such as AWS, Azure, or Google Cloud. Highlight specific projects where you leveraged cloud capabilities for model deployment or data processing.

Speak about the importance of ethical AI practices, such as fairness, accountability, and transparency. Share how you incorporate these elements into your projects, perhaps through diverse datasets and careful evaluation of model outcomes.

When answering this question, describe a specific algorithm you worked on and the improvements you implemented. Focus on the techniques you employed and the impact this had on the model’s performance.

List popular tools and frameworks you have experience with, such as TensorFlow, PyTorch, or Scikit-Learn. Explain your preferences and the reasons behind your choices, such as ease of use, community support, or specific project needs.

Share your strategies for staying informed about industry trends, such as following key publications, attending conferences, or participating in online forums. Emphasize your commitment to continuous learning and how it enhances your work.