Machine Learning Engineering Intern

As a Machine Learning Engineering Intern at Viant, you'll be responsible for collaborating with engineers and data scientists to tackle complex business problems utilizing machine learning techniques. You will assist in designing, developing, testing, and deploying scalable AI/ML services, as well as apply MLOps principles to build robust model lifecycle pipelines. Engaging in team discussions and reviews will enhance the quality of your contributions and foster a collaborative environment.

To be considered for the Machine Learning Engineering Intern position at Viant, candidates should be currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field. Strong programming skills in languages such as Python or Go, familiarity with machine learning frameworks like TensorFlow or PyTorch, and a passion for learning MLOps best practices are essential. Experience with distributed computing frameworks and exposure to cloud platforms like AWS or GCP are also beneficial.

The Machine Learning Engineering Internship at Viant runs for the full summer duration from June 16th to September 18th. This gives interns ample time to immerse themselves in real-world projects and gain valuable experience in the field of machine learning.

During your internship at Viant, you will work on impactful real-world projects that involve developing and deploying machine learning systems. You’ll contribute to problems that require processing hundreds of millions of events daily, experiment with cutting-edge Generative AI techniques, and assist in creating scalable AI/ML services that cater to high-throughput production workloads.

Yes, while the Machine Learning Engineering Internship at Viant is primarily in-person in our Irvine, CA office, there is flexibility for remote work on Fridays. This hybrid arrangement allows you to balance collaboration with your team on-site while enjoying the comfort of working from home one day a week.

To effectively answer this question, draw on specific projects where you've utilized TensorFlow or PyTorch. Discuss the types of models you built, any challenges you encountered, and how you resolved them. Make sure to highlight your understanding of how these frameworks fit into the machine learning lifecycle and the benefits they offer.

When discussing MLOps practices, emphasize your familiarity with version control for code, continuous integration/continuous deployment (CI/CD) pipelines, model monitoring, and lifecycle management. Provide specific examples of how you applied these practices, such as automating workflows or using tools like Docker or Jenkins in your projects.

Demonstrate your understanding of data privacy by discussing relevant regulations like GDPR or CCPA and how they apply to machine learning. Provide examples of how you've implemented practices to protect sensitive data, such as anonymization techniques, secure storage solutions, and ensuring compliance during data collection and model training.

Be prepared to share a specific challenge you encountered, such as an imbalanced dataset or a model that was not performing as expected. Discuss the steps you took to analyze the issue, the strategies you applied to address it, and the final outcome. Focus on the learning experience and how it has shaped your approach to problem-solving.

This is a great opportunity to convey your passion for the Ad Tech industry. Discuss your interest in how data and machine learning can drive marketing strategies, enhance user engagement, and optimize advertising performance. Relate this back to specific trends in the industry, such as personalized advertising or leveraging big data.

Describe your coding style as clean, organized, and modular. Discuss how you prioritize readability and maintainability. Provide examples of your practices, such as code reviews or adhering to coding standards, and how you ensure quality in collaborative projects.

Discuss the importance of collaboration and communication in successful machine learning projects. Provide examples of how you've worked together with cross-functional teams, utilized tools like Git or Slack for collaboration, and contributed to peer code reviews to improve overall project outcomes.

Share your methods for staying updated in the rapidly evolving field of machine learning and AI, such as following leading research papers, attending webinars, participating in online courses, or engaging with communities on platforms like GitHub or Kaggle. Highlight specific resources you find invaluable.

Articulate your desired role based on your strengths; whether it's coding, data analysis, or more of a leadership position. Discuss how you can contribute to the team's diversity of skills, your communicative approach to sharing ideas, and how you would support other team members in achieving project goals.

When outlining how to prepare a dataset, address key steps such as data cleaning (handling missing values, removing duplicates), exploratory data analysis (identifying patterns and distributions), feature selection/engineering, and splitting the dataset into training and testing sets. Discuss the importance of thorough preparation in creating a successful machine learning model.