AI Sr. Principal Engineer

Marvell Technology•Irvine, CA

1d•$202,900 - $300,260

About The Position

Marvell is building an AI ecosystem at the intersection of machine learning and hardware engineering, focused on analog and mixed-signal IC design. The systems will learn from real-world engineering outcomes and improve over time, using silicon as the ultimate feedback signal. The team brings together ML researchers, systems engineers, mathematicians, and circuit experts to build foundational technology intended to compound over years. The role may involve teaching machines to represent structured engineering artifacts such as graphs, netlists, layouts, and physical models; building learning and optimization systems that search large design spaces and converge on solutions verified against real physics; and designing scalable, reproducible data, modeling, and compute infrastructure. Other areas include creating learned models to approximate expensive physical simulations and developing agentic tools that assist with and increasingly automate expert engineering work. Candidates need deep expertise in at least one area, the ability to learn adjacent domains quickly, and an interest in building enduring technology. The expected base pay range is $202,900–$300,260 per annum. The position is eligible for Marvell bonus and equity plans, including a new-hire equity grant and annual equity refreshers. Starting pay depends on job-related skills, experience, qualifications, work location, and market conditions.

Requirements

MS or PhD in Computer Science, Electrical Engineering, Computer Engineering, Applied Mathematics, Physics, or an equivalent research or industry track record.
Deep expertise in at least one of machine learning, large-scale systems, applied mathematics or optimization, or analog/mixed-signal design.
Strong software fundamentals and fluency in Python; ability to turn research into robust, tested code.
Evidence of the ability to develop deep expertise in an unfamiliar domain, demonstrated through papers, projects, open-source work, or shipped systems.
A bias toward building durable technology through clean abstractions, reproducibility, and scale.
World-class depth in at least one area, range to learn an adjacent domain quickly, and ambition to build lasting technology.

Nice To Haves

Experience with PyTorch.
Background in sparse linear algebra, model compression or quantization, graph theory and spectral methods, convex or non-convex optimization, evolutionary computation, information or coding theory, compilers and intermediate representations, distributed systems, scientific or physics-informed machine learning, or theoretical and mathematical work applicable to structured physical domains.

Responsibilities

Develop systems that represent and understand structured engineering artifacts, including graphs, netlists, layouts, and physical models.
Build learning and optimization systems that search large design spaces and converge on solutions verified against real physics.
Design the data, modeling, and compute backbone to support scalable and reproducible systems.
Create fast learned models that approximate slow, expensive physical simulations.
Build agentic and tooling systems that assist with and increasingly automate expert engineering work.