Ph.D. Intern - AI/ML & Design Automation

## Your Team, Your Impact Marvell is building the silicon that makes AI possible — the custom XPUs, the 224G and 448G SerDes, the Silicon Photonics interconnects, the co-packaged optics platforms that hyperscalers depend on to train and deploy the world's most advanced models. Designing that silicon at the pace and complexity the AI era demands requires more than engineering talent. It requires intelligence applied to the design process itself. Marvell's AI and machine learning teams are working on exactly that — using AI to accelerate how silicon is designed, verified, and deployed, and building the enterprise AI infrastructure that makes Marvell's engineering organization faster and smarter at every level. This Ph.D. intern pool spans two distinct but connected tracks. The first is hardware-focused: applying ML and AI techniques directly to chip design challenges — EDA automation, design space exploration, predictive modeling for timing and power, and AI-driven approaches to physical design and verification at advanced process nodes. The second is enterprise-focused: building and deploying the internal AI tools and platforms — including large language model integrations, agentic workflows, and AI-assisted engineering systems — that Marvell's global engineering teams use every day. Both tracks sit at the frontier of what applied AI research looks like in a production semiconductor environment, and both are grounded in problems that do not yet have off-the-shelf solutions. ## What You Can Expect ## Track 1 — AI/ML for Hardware & Chip Design As our Ph.D. AI/ML Intern on the hardware track, every day you will apply machine learning research to real chip design problems across Marvell's advanced silicon development flow. Specifically, you can expect to: - Develop and apply ML models — including graph neural networks, reinforcement learning, and generative approaches — to chip design tasks such as placement, routing, timing closure, power estimation, and design rule checking - Work directly with production EDA tool flows and real design data from active tapeouts in 3nm and 2nm FinFET and Gate-All-Around processes - Build predictive models that reduce design iteration cycles and improve first-pass silicon success rates - Collaborate with analog, digital, and physical design engineers to identify high-value automation targets and validate model outputs against ground-truth silicon results - Present research findings and model performance to engineering leadership and contribute to internal technical documentation ## Track 2 — Enterprise AI Tools & Implementation As our Ph.D. AI/ML Intern on the enterprise tools track, every day you will work on the deployment and integration of large language models and agentic AI systems into Marvell's engineering workflows. Specifically, you can expect to: - Design, implement, and evaluate LLM-based tools and agentic workflows — including systems built on models such as Claude — for use by Marvell's global engineering and operations teams - Build retrieval-augmented generation (RAG) pipelines, fine-tuning workflows, and prompt engineering frameworks grounded in Marvell's internal knowledge and tooling ecosystem - Evaluate model performance, safety, and reliability in production enterprise environments and iterate based on real user feedback from engineering teams - Collaborate with IT, security, and engineering stakeholders to ensure responsible and scalable AI deployment across the organization - Present implementation results and adoption metrics to cross-functional leadership ## What We're Looking For To thrive in this role, you must have hands-on experience building and deploying machine learning systems — not just academic familiarity with the theory. Specifically: - Currently enrolled in a Ph.D. program in Computer Science, Electrical Engineering, Data Science, or a related field, with a research focus in machine learning, AI systems, or a related area - Demonstrate applied experience training, evaluating, and deploying ML models using frameworks such as PyTorch or TensorFlow - Write production-quality Python; familiarity with version control (Git) and software development best practices is required - Apply rigorous experimental methodology — you design experiments, measure results, and draw defensible conclusions from data - Communicate technical work clearly to both research and engineering audiences — you will present your work and defend your approach to the teams you work with