Research Intern, Physical AI and GPU (PhD)

Marvell is a global leader in high-performance silicon and AI infrastructure, powering the worlds most advanced datacenters, cloud platforms, 5G networks, and AI/ML workloads. Our innovations drive next-generation computing, networking, and storage - enabling breakthrough performance, scalability, and efficiency for the most demanding applications.

Why This Internship

Engage with open research problems at the frontier of AI systems - challenges that require access to real silicon, large-scale infrastructure, and production data
Contribute to work intended for publication, with support for presenting results both internally and externally
Translate research outcomes into real products and next-generation silicon, extending impact beyond publication
Collaborate with and receive mentorship from senior architects and researchers who will help advance your ideas
Gain access to advanced infrastructure, tools, and pre-release hardware rarely available in academic settings
Pursue original, exploratory research directions - independent thinking is actively encouraged

• Prototype and prove what next-generation Physical AI platforms can do
• Write and optimize CUDA to extract maximum performance from limited memory and low-end GPUs
• Build the compute, networking, and security platforms that autonomous systems run on
• Engineer agentic capabilities - systems that plan, decide, and act on their own
• Design and train ML models and SLMs for perception, reasoning, and action on real-world input (video, audio, sensors)

• Strong GPU and CUDA programming skills - kernel optimization, memory management, and performance tuning under tight resource constraints
• Strong fundamentals in machine learning, computer architecture, and systems
• Experience with robotic platforms or real-world AI applications (perception, sensors, or real-time control)
• Programming in C/C++ and Python
• Hands-on with ML modeling, training, and fine-tuning (PyTorch or TensorFlow)

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