Digital Signal Processing Intern

About Marvell

Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, automotive, 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 Central Engineering DSP Team develops and designs a variety of low power and high-speed Serializer-Deserializer (SerDes) IP in advanced CMOS technologies used in many products for the hyperscale cloud data center applications. You will join a team of expert DSP mixed-signal engineers developing 200G+ serial interfaces employing higher-order PAM modulations for electrical and optical channels. The candidate will also have an opportunity to experience work in industry, broaden the knowledge of key aspects of serial communication links.

What You Can Expect

• Collaborate with senior engineers to research and develop algorithms in areas such as digital communications, signal processing, and detection/estimation theory.
• Support simulation and modeling efforts using tools like MATLAB, Python, or C/C++ to evaluate system performance and validate design concepts.
• Assist in the exploration and implementation of machine learning techniques—including supervised and reinforcement learning—for applications in SerDes systems, adaptive filtering, and clock recovery.

What We're Looking For

• Candidate MUST be currently pursuing a MS or PhD degree (PhD preferred) in CS/EE or related technical field(s)
• Previous experience in industry is not required

• Excellent Research and Development experience in Probability Theory, Detection and Estimation, Digital Communications, Digital Signal Processing
• Strong analytical skills; Strong problem-solving skills
• Very good verbal and written communication skills
• Strong Programming skills in C/C++, MATLAB, or Python
• Knowledge of a SerDes system is a big plus
• Knowledge of reinforcement learning, and supervised learning is a plus
• Experience in adaptive filters, equalization and clock recovery is a plus

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.

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.

Interview Integrity

As part of our commitment to fair and authentic hiring practices, we ask that candidates do not use AI tools (e.g., transcription apps, real-time answer generators like ChatGPT, CoPilot, or note-taking bots) during interviews.

Our interviews are designed to assess your personal experience, thought process, and communication skills in real-time. If a candidate uses such tools during an interview, they will be disqualified 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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