Ph.D. Intern AIML & Design Automation

About Marvell

Marvells 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.

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 worlds 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. Marvells 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 Marvells 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 Marvells 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.

Marvells Ph.D. Intern Program places doctoral candidates directly inside these active efforts working on problems that are inseparable from their academic research. The work done here is the applied dimension of doctoral research in machine learning computer science and electrical engineering conducted at production scale on real design data with real consequences for the silicon that ships to the worlds largest AI infrastructure operators. What you will take away is something no coursework or academic dataset can replicate: the experience of deploying your research inside one of the most complex engineering environments in the semiconductor industry.

What You Can Expect

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 Marvells 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

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 Marvells 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 Marvells global engineering and operations teams

• Build retrieval-augmented generation (RAG) pipelines fine-tuning workflows and prompt engineering frameworks grounded in Marvells 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 Were 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

Track 1 Additional Requirements

• Coursework or research experience in VLSI design digital or analog circuit design computer architecture or EDA sufficient to understand the design problems your models are solving

• Familiarity with graph-based ML methods (GNNs) reinforcement learning or generative models applied to structured engineering data

• Exposure to EDA tools or chip design flows (Cadence Synopsys or equivalent) is a strong plus

Track 2 Additional Requirements

• Design and implement agentic GenAI systems with demonstrated experience across the full stack LLMs multimodal models RAG pipelines and agentic protocols such as MCP and A2A

• Apply hands-on knowledge of SOTA architectures and frameworks including transformers diffusion models and orchestration tools such as LangChain LangGraph AutoGen CrewAI LlamaIndex or Hugging Face

• Benchmark and evaluate model performance rigorously you identify failure modes propose enhancements and back conclusions with data

Preferred Qualifications Track 2

• Experience with agentic reasoning planning and tool-use patterns in multi-agent orchestration frameworks such as n8n or AutoGen

• Exposure to end-to-end data pipeline development and model deployment in collaboration with data engineering or platform teams

• Demonstrated ability to independently research and implement concepts from current AI literature and apply them in a working system

Expected Base Pay Range (USD)

37 - 73 $ per hour.

The successful candidates starting base pay will be determined based on job-related skills experience qualifications work location and market conditions. The expected base pay range for this role may be modified based on market conditions.

Additional Compensation and Benefit Elements

Marvell is committed to providing exceptional comprehensive benefits that support our employees at every stage - from internship to retirement and through lifes most important moments. Our offerings are built around four key pillars: financial well-being family support mental and physical health and recognition. Highlights for our interns: medical dental and vision coverage perks and discounts robust mental health resources to prioritize emotional well-being and paid holidays. Additional compensation may be available for intern PhD candidates. We look forward to sharing more with you during the interview process.

All qualified applicants will receive consideration for employment without regard to race color religion sex national origin sexual orientation gender identity disability or protected veteran status.

Any applicant who requires a reasonable accommodation during the selection process should contact Marvell HR Helpdesk at .

Interview Integrity

To support fair and authentic hiring practices candidates are not permitted to use AI tools (such as transcription apps real-time answer generators like ChatGPT or Copilot or automated note-taking bots) during interviews.

These tools must not be used to record assist with or enhance responses in any way. Our interviews are designed to evaluate your individual experience thought process and communication skills in real time. Use of AI tools without prior instruction from the interviewer will result in disqualification from the hiring process.

This position may require access to technology and/or software subject to U.S. export control laws and regulations including the Export Administration Regulations (EAR). As such applicants must be eligible to access export-controlled information as defined under applicable law. Marvell may be required to obtain export licensing approval from the U.S. Department of Commerce and/or the U.S. Department of State. Except for U.S. citizens lawful permanent residents or protected individuals as defined by 8 U.S.C. 1324b(a)(3) all applicants may be subject to an export license review process prior to employment.

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