Senior Machine Learning Engineer - Cloud AI Platform/Infrastructure - job 13 of 20

As a Senior Machine Learning Engineer at Visa, you will primarily focus on extending our AI as a Service platform to the Public Cloud. This includes optimizing the existing codebase, enhancing development procedures, and creating innovative solutions in AI and machine learning. Your role will involve a lot of hands-on development, making it imperative to have a strong background in both public cloud technologies and AI/ML production systems.

To qualify for the Senior Machine Learning Engineer role at Visa, candidates should have a solid development background, especially in cloud technologies and AI/ML. Experience in production systems related to machine learning is crucial, along with a strong problem-solving mentality and the ability to deliver innovative solutions within a collaborative environment.

Yes! The Senior Machine Learning Engineer position at Visa is a hybrid role, offering flexibility. The expectation of days in the office will be discussed and confirmed by your hiring manager, allowing you to maintain a balance that suits your lifestyle.

Visa prides itself on offering ample opportunities for learning and growth for its Senior Machine Learning Engineers. Working on cutting-edge projects in AI technology within the payments space allows you to deepen your knowledge, enhance your skills, and make significant contributions that could lead to future leadership roles.

As a Senior Machine Learning Engineer at Visa, you will play a pivotal role in delivering innovative AI solutions that impact thousands of users globally. Your contributions to our AI as a Service platform will not only enhance how we operate but also contribute to creating smarter payment solutions that drive the company forward.

When optimizing machine learning models, it’s essential to analyze model performance metrics like accuracy, precision, and recall to identify areas for improvement. You should also leverage techniques like hyperparameter tuning, feature engineering, and model selection to enhance performance.

Discuss the specific cloud platforms you’ve worked with, detailing specific projects where you deployed machine learning models on cloud infrastructure. Highlight your familiarity with services like AWS, Google Cloud, or Azure, especially in AI/ML applications.

Identify a specific challenge, such as data quality or model scalability, and explain how you approached it. Discuss the strategies you implemented to overcome the challenge, emphasizing your problem-solving capabilities.

Interpretability can be crucial, especially in a commercial environment. Discuss your techniques for model transparency, such as using simpler models, or employing tools like SHAP or LIME to provide insights into predictions.

Overfitting can be mitigated through methods like cross-validation, regularization techniques, and employing dropout layers in neural networks. Explain your experience with these techniques and how they proved effective in your models.

Walk through the end-to-end process from model training in an experimental environment to transferring the model to a production system. Discuss the importance of monitoring model performance and setting up a feedback loop for continuous improvement.

Share your dedication to continuous learning through resources such as research papers, online courses, and tech conferences. Mention any particular communities or platforms you engage with to stay updated.

Provide a concise overview of a specific project where you played a leading role. Discuss the problem you were solving, the methodologies you employed, and quantify the successful outcomes achieved.

Data preprocessing is critical as it ensures your data is clean, consistent, and formatted correctly for training. Discuss techniques you've used like normalization, encoding categorical variables, and handling missing values.

Explain the methods you employ for feature selection such as filter methods, wrapper methods, or embedded methods. Provide insights into how careful feature selection can greatly enhance model accuracy and performance.