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Santa Clara, CA

Internship

Onsite

$27/hr - $55/hr

Intern

Marvell Technology is a leading semiconductor solutions provider focused on innovative technology across enterprise, cloud, and AI sectors. The AI Engineer Intern will contribute to the design, implementation, and optimization of AI agents and frameworks, working closely with cross-functional teams to develop impactful technologies that improve business outcomes.

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Responsibilities

Lead the design and development of Agentic AI applications and workflows for real-world use cases
Conduct research and experiments on new AI agent architectures and orchestration strategies
Implement and optimize machine learning models and agent pipelines for performance and scalability
Develop prototypes and proof-of-concept AI agents capable of performing multi-step tasks
Benchmark and analyze the performance of existing AI agents, proposing enhancements to improve reliability and effectiveness
Work with data engineers and software developers to integrate agents into larger platforms and applications
Support the development of secure and compliant agent-based workflows that handle sensitive data
Stay up-to-date with advancements in agentic AI, machine learning, and automation frameworks and apply them to ongoing projects

Qualification

Required

Currently pursuing a bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, or other related fields with an anticipated graduation date between Winter 2026 and Spring 2027
Proficiency in Python, R, and Java
Solid understanding of machine learning concepts, algorithms, and frameworks such as TensorFlow and PyTorch
Prior exposure to data analysis, statistical modeling, and experimental design
Knowledge of software development best practices, including version control (e.g., Git)
Strong problem-solving skills and ability to work in a collaborative team environment

Preferred

Previous experience with AI/ML projects, especially agent-based AI development
Familiarity with advanced topics such as deep learning, reinforcement learning, or natural language processing
Exposure to agent orchestration frameworks (e.g., LangChain, CrewAI, Semantic Kernel)
Familiarity with cloud computing services (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes)
Understanding of DevOps principles and experience with CI/CD pipelines
Experience optimizing machine learning models for deployment and scalability
Familiarity with research tools and libraries (e.g., SciPy, Keras, Hugging Face Transformers)