AI Sr. Principal Engineer

Marvell Technology • Irvine, CA

3d • $202,900 - $300,260

About The Position

Marvell is building an enduring AI ecosystem at the intersection of machine learning and hardware engineering, focused on systems that learn from real-world engineering outcomes and improve over time. The work centers on analog/mixed-signal IC design, a sparse, structured, physics-governed domain in which silicon provides the ultimate feedback signal. The team brings together ML researchers, systems engineers, mathematicians, and circuit experts to develop foundational technology intended to compound over time.

The role involves developing systems that represent structured engineering artifacts such as graphs, netlists, layouts, and physical models; building learning and optimization systems that explore design spaces and converge on solutions verified against real physics; and creating the data, modeling, and compute backbone needed for scalable, reproducible work. It also includes developing learned models that approximate expensive physical simulations and building agentic tools to assist with—and increasingly automate—expert engineering work. Candidates are expected to bring deep expertise in at least one relevant area, learn adjacent domains quickly, and contribute to durable technology.

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/optimization, or analog/mixed-signal design.
• Strong software fundamentals.
• Fluency in Python.
• Ability to turn research into robust, tested code.
• Evidence of being able to develop deep expertise in an unfamiliar domain, demonstrated through papers, projects, open source, or shipped systems.
• Interest in building durable systems with clean abstractions, reproducibility, and scale.

Nice To Haves

• Experience with PyTorch.
• Relevant expertise or background in sparse linear algebra, model compression or quantization, graph theory and spectral methods, convex or non-convex optimization, evolutionary computation, information and 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 machine learning 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 scalable, reproducible data, modeling, and compute infrastructure.
• Create fast learned models that approximate slow, expensive physical simulations.
• Build agentic systems and tools that assist with and increasingly automate expert engineering work.
• Translate research into robust, tested software and build durable abstractions and systems.

Benefits

• Eligible to participate in bonus and equity plans, including a new-hire equity grant and annual equity refreshers.
• Employee stock purchase plan with a 2-year look back.
• Family support programs.
• Mental health resources.
• Recognition and service awards.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior