Machine Learning Engineer Intern, BS/MS - Summer 2027

Marvell Technology•Santa Clara, CA

3d•$29 - $57

About The Position

Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities. At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead. The Machine Learning Platform team develops and operates the infrastructure that powers large-scale AI and machine learning workloads. The team focuses on enabling scalable, reliable, and efficient ML operations across distributed computing environments, supporting model training, deployment, monitoring, and optimization. Working at the intersection of AI/ML and systems engineering, the team drives performance, resource efficiency, and operational excellence for next-generation AI platforms.

Requirements

Currently pursuing a Bachelor’s or Master's degree in Computer Science, Computer Engineering, or a related technical field with an expected graduation date between Fall 2027 and Summer 2028
Proficiency in Python for manipulation and analysis
Knowledge of GPU computing, CUDA programming, networking fundamentals, and distributed systems concepts
Familiarity with distributed computing environments, cloud platforms (AWS, GCP, Azure), and containerization technologies such as Docker and Kubernetes
Understanding of system monitoring, logging, and performance analysis concepts
Strong analytical and problem-solving skills, with experience in data analysis and visualization
Exposure to machine learning workflows, model deployment, and lifecycle management.
Strong written and verbal communication skills, with the ability to present technical findings to diverse stakeholders

Responsibilities

Monitor and analyze AI platform performance across distributed computing environments.
Identify opportunities to optimize machine learning training and inference workloads.
Support GPU cluster and cloud infrastructure capacity planning and resource management.
Evaluate model deployment performance, including latency, throughput, and scalability metrics.
Troubleshoot system performance issues and infrastructure challenges impacting ML workloads.
Collaborate with cross-functional engineering teams to improve AI infrastructure and platform efficiency.
Enhance data pipeline and storage performance for large-scale machine learning applications.