Research Intern, Physical AI and GPU (PhD)

Marvell — Hyderabad, Telangana

₹2.5–4.9 LPA (HireSetu estimate) · Entry level · Freshers eligible · Internship · onsite

HireSetu listing reference 64a1b2 — confirm requirements below, then apply on the employer site.

Quick answer: A PhD-level research internship in Hyderabad, with publication expected and pre-release silicon in reach. CUDA optimisation under genuinely tight memory and compute limits is the skill that decides it, alongside hands-on robotics or sensor work.

From Marvell's job posting

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 a global leader in high-performance silicon and AI infrastructure, powering the world's 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

What You Can Expect

Focus: Build the platforms and infrastructure that Physical AI runs on — the intelligence, networking, and silicon that let autonomous systems perceive, reason, and act in the real world, on tightly constrained compute.

Key Responsibilities

  • 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)

What We're Looking For

Eligibility: Enrolled in a PhD, MS by Research, or MTech by Research program in Computer Science, Computer Engineering, electronics or Electrical Engineering, Robotics or a related field.

Requirements

  • 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)

Preferred: Exposure to model optimization and quantization for constrained hardware, robotics frameworks (e.g., ROS), on-device inference, agentic systems, or next-generation AI silicon.





Additional Compensation and Benefit Elements

With competitive compensation and great benefits, you will enjoy our workstyle within an environment of shared collaboration, transparency, and inclusivity. We’re dedicated to giving our people the tools and resources they need to succeed in doing work that matters, and to grow and develop with us. For additional information on what it’s like to work at Marvell, visit our Careers page.

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.

#LI-TT1

The employer’s company overview, benefits and equal-opportunity statement are left out here; they are on the original posting.

Text from Marvell's official job posting, formatted by HireSetu. The employer's own page is the final word on details.

HireSetu's note

Small GPUs, hard limits

The listing says it plainly: extract maximum performance from limited memory and low-end GPUs. That is the opposite of the usual research setup where you queue for a big cluster and stop worrying about bytes. Expect real time on memory reuse, kernel efficiency and how far a model can be cut down before it breaks. Quantisation and on-device inference sit in the preferred list for a reason. If you have tuned a kernel, know the before and after numbers cold: cycles, memory traffic, accuracy lost.

Papers and products in the same loop

Marvell states the work is intended for publication and also meant to land in products and future silicon. That shapes the calendar, because a result that looks good in a benchmark has to survive on the hardware it was built for. Pre-release hardware and mentorship from senior architects are the parts an academic lab cannot offer. Ask which project you would join, since the scope across perception, agentic systems and networking platforms is wide.

Robotics work carries real weight here

Perception, sensors and real-time control are listed as requirements, and ROS as preferred. Sensor pipelines and control loops come with latency budgets and messy inputs that a clean dataset never shows you. If your PhD work is mostly simulation or benchmark accuracy, say that plainly and describe anything physical you have touched: a robot, a camera rig, an embedded board.

Two clauses to read twice

Marvell bars AI tools during interviews and states that using them means disqualification, so interview unaided. On eligibility, you need to be enrolled in a PhD, MS by Research or MTech by Research programme in computer science, computer engineering, electronics or electrical engineering, robotics or something close. The export control note means applicants who are not US citizens, lawful permanent residents or protected individuals may face a licence review before joining, which can add time to an offer.

Drafted with AI from Marvell's job posting and checked by Prashanth Jakkula before publishing. See our Editorial Policy.

How to apply

  1. Open Marvell's careers site with the Apply button.
  2. Create a Workday account for Marvell. Workday logins are separate for every company, so an account made for another employer won't work here.
  3. Upload your resume and check every auto-filled field; Workday often misreads dates and job titles.
  4. Answer the screening questions, submit, and look for the confirmation email from Workday.

Apply on Marvell's site

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