GenAI & AI/ML Engineering Intern

Company Description

Sandisk understands how people and businesses consume data and we relentlessly innovate to deliver solutions that enable today’s needs and tomorrow’s next big ideas. With a rich history of groundbreaking innovations in Flash and advanced memory technologies, our solutions have become the beating heart of the digital world we’re living in and that we have the power to shape.

Sandisk meets people and businesses at the intersection of their aspirations and the moment, enabling them to keep moving and pushing possibility forward. We do this through the balance of our powerhouse manufacturing capabilities and our industry-leading portfolio of products that are recognized globally for innovation, performance and quality.

Sandisk has two facilities recognized by the World Economic Forum as part of the Global Lighthouse Network for advanced 4IR innovations. These facilities were also recognized as Sustainability Lighthouses for breakthroughs in efficient operations. With our global reach, we ensure the global supply chain has access to the Flash memory it needs to keep our world moving forward.

Job Description

We are seeking a highly motivated GenAI & AI/ML Engineering Intern to join our Design Enablement & Automation (DE&A) team. This role offers an exciting opportunity to revolutionize Sandisk’s semiconductor development workflows through cutting-edge AI technologies.

ESSENTIAL DUTIES AND RESPONSIBILITIES:

Generative AI:

• Develop and deploy large language models (LLMs) inference pipelines for electronic design automation
• Design Retrieval-Augmented Generation (RAG) systems and semantic search capabilities for engineering documentation management
• Implement conversational AI agents for technical support
• Develop prompt engineering strategies and fine-tuning approaches for domain-specific AI assistants
• Build multimodal AI systems combining text, code, and design diagrams
• Implement model versioning, A/B testing, and performance monitoring for GenAI systems
• Optimize GenAI model performance for production environments
• Build evaluation frameworks for generative AI model quality and safety

AI/ML & Data Intelligence:

• Develop machine learning models for design optimization and prediction
• Build automated data pipelines and workflows for AI model training and inference
• Create predictive analytics solutions using both traditional ML and generative approaches
• Implement AI-driven anomaly detection and pattern recognition systems
• Build dashboards and visualizations enhanced with natural language generation

Learning Opportunities:

• Hands-on experience with state-of-the-art commercial and open-source generative AI models
• Deep dive into RAG architectures, vector databases, and semantic search technologies
• Exposure to enterprise-scale GenAI infrastructure and LLMOps practices
• Experience with cutting-edge semiconductor design flows and tools enhanced by AI
• Mentorship from senior engineers specializing in both GenAI and semiconductor design domains
• Opportunity to contribute to GenAI development and present at AI/semiconductor conferences

Qualifications

Required:

• Currently enrolled in a Bachelor's, Master's, or Ph.D. program in Computer Science, Electrical Engineering, Computer Engineering, Data Science, or related technical field
• Strong programming skills in Python and experience with AI and data analytics frameworks (Spark, Ray, pandas, scikit-learn, PyTorch, OpenAI API, or similar)
• Fundamental understanding of machine learning and generative AI concepts and algorithms
• Experience with data structures, algorithms, and software engineering principles
• Basic knowledge of natural language processing (NLP) and Generative AI concepts
• Familiarity with version control systems (Git) and collaborative development workflows
• Curiosity about applying AI to semiconductor design and engineering workflows
• Ability to quickly learn new technologies and adapt to evolving AI landscape
• Strong attention to detail and commitment to code quality and documentation

Preferred:

• Hands-on experience with large language models (LLMs) and generative AI frameworks
• Knowledge of Retrieval-Augmented Generation (RAG) systems and vector databases
• Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes)
• Familiarity with MLOps practices, model deployment, and monitoring tools
• Understanding of prompt engineering and fine-tuning techniques for language models
• Experience with data pipeline tools (Apache Airflow) and workflow orchestration
• Knowledge of semiconductor design flows, electronic