Senior Machine Learning Engineer- Machine Learning Platform - job 7 of 11

As a Senior Machine Learning Engineer at Visa, your main responsibilities include monitoring the infrastructure health, problem-solving urgent issues, platform stabilization through unit and integration testing, and interfacing with both Kubernetes and data platform teams. Additionally, you will implement new infrastructure features that enhance performance and ensure the seamless operation of Visa's Machine Learning Platform.

To excel as a Senior Machine Learning Engineer at Visa, candidates should possess a strong background in machine learning and software development, particularly with frameworks such as TensorFlow and PyTorch. Proficiency in programming languages like Python and Go is essential. Experience with distributed systems and cloud computing, particularly Kubernetes, is also crucial. A degree in Computer Science, Engineering, or a related field, along with professional experience in machine learning and data infrastructure, will prepare you for this role.

In your role as a Senior Machine Learning Engineer at Visa, you will work with a range of state-of-the-art technologies, including Kubernetes for container orchestration, Ray for distributed computing, and frameworks like TensorFlow, Torch, and XGBoost for machine learning. Familiarity with Apache Spark for big data processing will also be beneficial. This diverse tech stack will enable you to support Visa's data scientists and enhance machine learning capabilities.

The Senior Machine Learning Engineer position at Visa is classified as a hybrid role. This means that while you will have the flexibility to work from home, there will also be requirements to work in the office on certain days. The specific number of in-office days will be confirmed by your hiring manager, allowing for a balance between collaboration in-person and the convenience of remote work.

At Visa, the culture of growth is deeply embedded in our values. For Senior Machine Learning Engineers, we prioritize your professional development through continuous learning opportunities, mentorship programs, and participation in innovative projects. Our collaborative environment encourages you to share your ideas and insights while also benefiting from the experiences of your peers, ensuring that you can thrive both personally and professionally.

In your answer, detail any projects you have worked on utilizing TensorFlow or PyTorch, discussing specific applications, the type of models developed, and the impact of these projects. Highlight your understanding of the frameworks' strengths and any challenges you overcame while using them.

Discuss your experience with monitoring tools or techniques you've used to ensure the health of a machine learning infrastructure. Explain how you identify issues, the steps you take to troubleshoot, and how you prioritize problem-solving to minimize disruption.

Describe your experience with Kubernetes, explaining how it facilitates scalability and management of containerized applications. Use examples from previous projects to illustrate how you've leveraged Kubernetes in deploying machine learning models.

Choose a specific project that had significant challenges, such as unexpected technical issues or tight deadlines. Describe the situation, the steps you took to address the challenges, and the outcome. Highlight the skills and tools you utilized during the process.

Explain your strategy for unit and integration testing within machine learning contexts. Discuss the different testing techniques you've implemented and how they help ensure the reliability and performance of machine learning models.

Share specific examples of how you’ve utilized distributed computing frameworks like Ray to enhance model training and scalability. Discuss the benefits you've experienced and any key learnings from your work in this area.

Outline your general approach to feature implementation within an established platform. Discuss steps such as requirements gathering, stakeholder involvement, development methodologies, and deployment strategies. Examples from prior experiences would enhance your answer.

Emphasize the importance of communication and teamwork when working with cross-functional teams. Share specific strategies you've employed to foster collaboration and share insights among data science teams, infrastructure teams, and other stakeholders.

Discuss key metrics relevant to your projects, such as accuracy, precision, recall, or F1 score, and explain why they are important. Relate your choices to the goals of the project and how they impact decision-making.

In your response, convey your passion for machine learning and how it aligns with Visa’s mission. Highlight how you admire Visa's dedication to innovation and financial inclusion and how you believe your skills can contribute to shaping the future of money movement.