Ph.D. Intern – AI/ML, Design Automation

Jobtailor; Arizona; On-site; USD 120,000 - 180,000; Full time

Job summary

Marvell is seeking a PhD-focused machine learning researcher to tackle design-automation challenges in cutting-edge silicon development. You will develop ML models for placement, routing, and timing, leveraging graph neural networks, reinforcement learning, and generative approaches.

You will work with Cadence/Synopsys flows and real data from 3nm/2nm tapeouts, iterating toward first-pass silicon success. You will build LLM-based tools and RAG pipelines, evaluate model performance, and

Qualifications

• PhD candidate in CS/EE/Data Science or related field with ML focus.
• Hands-on experience building and deploying ML systems.
• Applied experience training, evaluating, and deploying ML models using PyTorch or TensorFlow.
• Production-quality Python.
• Familiarity with Git and software development best practices.
• Rigorous experimental methodology and ability to measure results and draw defensible conclusions from data.
• Ability to communicate technical work clearly to research and engineering audiences.
• Track 1: Coursework or research in VLSI design, digital/analog circuit design, or EDA.
• Track 1: Familiarity with graph-based ML methods, RL, or generative models for structured engineering data.
• Track 1: Exposure to EDA tools such as Cadence or Synopsys.
• Track 2: Experience with LLMs, multimodal models, RAG pipelines, and agentic protocols (MCP, A2A).
• Track 2: Knowledge of transformers, diffusion models, and orchestration tools (LangChain, LangGraph, AutoGen, CrewAI, LlamaIndex, Hugging Face).
• Track 2: Model benchmarking and evaluating, including failure modes and enhancements.
• Track 2: End-to-end data pipeline development and deployment with data engineering teams.

Responsibilities

• Apply machine learning research to real chip design problems across Marvell's advanced silicon development flow.
• Develop and apply ML models, including graph neural networks, reinforcement learning, and generative approaches, to placement, routing, timing closure, power estimation, and design rule checking.
• Work with production EDA tool flows and real design data from active tapeouts in 3nm and 2nm processes.
• Build predictive models to reduce design iteration cycles and improve first-pass silicon success rates.
• Collaborate with analog, digital, and physical design engineers and validate model outputs against ground-truth silicon results.
• Present research findings and model performance to engineering leadership and contribute to internal technical documentation.
• Design, implement, and evaluate LLM-based tools and agentic workflows for global engineering and operations teams.
• Build RAG pipelines, fine-tuning workflows, and prompt engineering frameworks using Marvell's internal knowledge and tooling ecosystem.
• Evaluate model performance, safety, and reliability in production enterprise environments and iterate based on engineering-team feedback.
• Collaborate with IT, security, and engineering stakeholders on responsible and scalable AI deployment.
• Present implementation results and adoption metrics to cross-functional leadership.

Skills

Machine Learning

Graph Neural Networks

Reinforcement Learning

Generative Models

Python

PyTorch

TensorFlow

Model Evaluation

Git

Communication

Education

PhD candidate in CS/EE/Data Science or related

Tools

Cadence

Synopsys

LangChain

Hugging Face

AutoGen

CrewAI

LlamaIndex

N8n

MCP

A2A

Job description

• Apply machine learning research to real chip design problems across Marvell's advanced silicon development flow
• Develop and apply ML models, including graph neural networks, reinforcement learning, and generative approaches, to placement, routing, timing closure, power estimation, and design rule checking
• Work with production EDA tool flows and real design data from active tapeouts in 3nm and 2nm processes
• Build predictive models to reduce design iteration cycles and improve first-pass silicon success rates
• Collaborate with analog, digital, and physical design engineers and validate model outputs against ground-truth silicon results
• Present research findings and model performance to engineering leadership and contribute to internal technical documentation
• Design, implement, and evaluate LLM-based tools and agentic workflows for global engineering and operations teams
• Build RAG pipelines, fine-tuning workflows, and prompt engineering frameworks using Marvell's internal knowledge and tooling ecosystem
• Evaluate model performance, safety, and reliability in production enterprise environments and iterate based on engineering-team feedback
• Collaborate with IT, security, and engineering stakeholders on responsible and scalable AI deployment
• Present implementation results and adoption metrics to cross-functional leadership

Requirements

• 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
• Hands-on experience building and deploying machine learning systems
• Applied experience training, evaluating, and deploying ML models using frameworks such as PyTorch or TensorFlow
• Production-quality Python
• Familiarity with Git and software development best practices
• Rigorous experimental methodology and ability to measure results and draw defensible conclusions from data
• Ability to communicate technical work clearly to research and engineering audiences and present and defend work
• Track 1: Coursework or research experience in VLSI design, digital or analog circuit design, computer architecture, or EDA
• Track 1: Familiarity with graph-based ML methods, reinforcement learning, or generative models applied to structured engineering data
• Track 1: Exposure to EDA tools or chip design flows such as Cadence or Synopsys is a strong plus
• Track 2: Demonstrated full-stack experience with LLMs, multimodal models, RAG pipelines, and agentic protocols such as MCP and A2A
• Track 2: Hands-on knowledge of transformers, diffusion models, and orchestration tools such as LangChain, LangGraph, AutoGen, CrewAI, LlamaIndex, or Hugging Face
• Track 2: Rigorous model benchmarking and evaluation, including identifying failure modes and proposing enhancements
• Track 2 preferred: Experience with agentic reasoning, planning, and tool-use patterns in multi-agent orchestration frameworks such as n8n or AutoGen
• Track 2 preferred: Exposure to end-to-end data pipeline development and model deployment with data engineering or platform teams
• Track 2 preferred: Ability to independently research and implement concepts from current AI literature
• Applicants must be eligible to access export-controlled information under U.S. export control laws; some applicants may require export license review

Core Competencies

Demonstrates expertise in machine learning model development and deployment, particularly in the context of chip design and EDA tools. Proficient in Python programming and familiar with frameworks such as PyTorch and TensorFlow, with a strong emphasis on collaboration and communication within cross-functional teams.

Highest-signal resume keywords

• Machine Learning Model Development
• Python Programming
• Graph Neural Networks
• EDA Tool Experience
• Reinforcement Learning

Hard Skills

• Machine Learning
• Graph Neural Networks
• Reinforcement Learning
• Generative Models
• Python
• PyTorch
• TensorFlow
• EDA Tools
• VLSI Design
• Model Evaluation

Soft Skills

• Communication
• Collaboration
• Problem-Solving
• Research Skills
• Presentation Skills

Industry Keywords

• Chip Design
• Silicon Development
• EDA
• AI Systems
• Export Control Laws

Tools & Technologies

• Cadence
• Synopsys
• LangChain
• Hugging Face
• AutoGen
• CrewAI
• LlamaIndex
• N8n
• MCP
• A2A