Lead Machine Learning Engineer- Machine Learning Platform - job 7 of 21

As a Lead Machine Learning Engineer at Visa, your primary responsibilities will include monitoring infrastructure health and addressing urgent issues, stabilizing the platform through effective testing, and collaborating with the Kubernetes and data platform teams. You will also implement new infrastructure features, mentor junior developers, and engage in the architectural design of projects before implementation.

To succeed as a Lead Machine Learning Engineer at Visa, candidates should have substantial experience in machine learning and software engineering, particularly with frameworks like Kubernetes, Ray, Torch, and TensorFlow. Proficiency in programming languages such as Python and Go is essential, along with a strong understanding of distributed computing. A background in mentoring and the ability to interface with cross-functional teams are also important qualifications.

In the role of Lead Machine Learning Engineer at Visa, you will work with an impressive stack of technologies that includes Kubernetes, Ray, Torch, TensorFlow, XGBoost, and Spark. You'll leverage these tools to develop and maintain a distributed computing environment that empowers Visa's data scientists and researchers.

The Lead Machine Learning Engineer position at Visa is a hybrid role, with specific in-office workdays determined by your hiring manager. This setup allows for both collaboration in-person and the flexibility of remote work.

As a Lead Machine Learning Engineer at Visa, your work will directly support the mission of financial inclusion. By providing robust infrastructure and developing advanced machine learning models, you'll enable better financial services for billions, helping to design solutions that unlock financial access and improve money movement globally.

Be prepared to share specific instances where you've utilized frameworks like Kubernetes, Spark, or Ray. Discuss the challenges you faced and how you addressed them, using quantifiable outcomes to demonstrate your impact.

Discuss your approach to monitoring infrastructure health, your exposure to unit and integration testing, and your strategies for identifying and solving issues proactively. Provide examples from your past roles.

Highlight your proficiency in Python and how it's been integral in building machine learning models and managing data pipelines. Illustrate your experience with specific libraries or frameworks within Python.

Share a specific example that demonstrates your mentoring approach, the challenges you faced, and the positive outcomes that resulted from your guidance. Emphasize the value you added to their learning experience.

Explain your process for engaging in architectural discussions, how you consider scalability and performance, and any frameworks or practices you follow. Provide examples of designs you’ve developed.

Discuss methods you use to evaluate model performance, such as monitoring metrics or conducting error analysis. Share how you refine models based on these insights.

Talk about resources you utilize—such as journals, online courses, or conferences—to stay current with industry trends and how you apply new knowledge to your work.

Clearly define both concepts, offering examples of when each is appropriate to use. Discuss practical applications you have worked on that involved these learning strategies.

Focus on a collaborative project, detailing how you effectively communicated and coordinated tasks between teams. Highlight the importance of building relationships and understanding varying perspectives.

Reflect on potential scaling issues such as data processing, model management, or integration with existing systems, and discuss strategies you've used to mitigate these challenges in the past.