Hiring.Camp

AI Sr. Principal Engineer

Marvell

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Yesterday

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Location
US-CA - Irvine, United States of America
Type
Full-time
Department
Engineering
Seniority
Lead
Education
PhD
Source
Workday

Description

About Marvell

Marvell’s 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

We're building an enduring AI ecosystem at the intersection of machine learning and hardware engineering — systems that learn from how engineering actually plays out in the real world, and get measurably better over time. The domain is analog/mixed-signal IC design: a sparse, deeply structured, physics-governed problem where the ultimate feedback signal is silicon itself. We're assembling an unusually broad team — ML researchers, systems engineers, mathematicians, and circuit experts — to build foundational technology meant to compound for years, not ship a one-off tool.

What You Can Expect

- Teaching machines to represent and understand structured engineering artifacts (graphs, netlists, layouts, physical models).
- Building learning and optimization systems that search vast design spaces and converge on solutions verified against real physics.
- Designing the data, modeling, and compute backbone that lets all of the above scale and stay reproducible.
- Creating fast learned models that stand in for slow, expensive physical simulation.
- Building the agentic and tooling layer that lets these systems assist — and increasingly automate — expert engineering work.

You don't need to be an expert in all of it. You need world-class depth in something, the range to learn an adjacent domain fast, and the ambition to build something lasting.

Backgrounds that aren't the "obvious" fit but map onto this problem: sparse linear algebra, model compression/quantization, graph theory and spectral methods, convex & non-convex optimization, evolutionary computation, information & coding theory, compilers and intermediate representations, distributed systems, scientific/physics-informed ML, and theoretical/mathematical work that you suspect "should" apply to structured physical domains. If you've ever looked at circuits — or any sparse, relational, physical system — and seen math others didn't, we want to talk.

What We're Looking For

·       MS/PhD in CS, EE, CompE, Applied Math, Physics, or equivalent research/industry track record.

·       Deep expertise in at least one of: machine learning, large-scale systems, applied mathematics/optimization, or analog/mixed-signal design.

·       Strong software fundamentals; fluent in Python (PyTorch a plus); comfortable turning research into robust, tested code.

·       Evidence you can go deep in an unfamiliar domain — papers, projects, open source, or shipped systems.

·       Bias toward building things that endure: clean abstractions, reproducibility, scale.

Expected Base Pay Range (USD)

202,900 - 300,260, $ per annum

The successful candidate’s 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.

This role is eligible to participate in Marvell's bonus and equity plans, including a new hire equity grant and annual equity refreshers, ensuring you are rewarded for your contributions and remain invested in the company's long-term success.

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 life’s most important moments. Our offerings are built around four key pillars: financial well-being, family support, mental and physical health, and recognition. Highlights include an employee stock purchase plan with a 2-year look back, family support programs to help balance work and home life, robust mental health resources to prioritize emotional well-being, and a recognition and service awards to celebrate contributions and milestones. 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 [email protected].

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-TD1

Skills

PythonMachine LearningPyTorch

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