Principal Network Engineer/AI Fabrics Architect, AI Solutions Lab

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

The Solutions Architecture team sits within Marvell's Platform organization, which turns advanced silicon into deployable, customer-ready AI and cloud infrastructure. This team — the AI Solutions Lab — owns the validation layer that proves our platforms perform as promised, across compute, networking, and memory fabrics. Without it, Marvell could design great silicon but could never prove, at rack scale, that it performs the way customers expect — and hyperscalers don't buy chips on paper, they buy validated systems.
Day to day, the team runs AI benchmarking experiments that mirror real customer workloads, operates GPU/HPC lab clusters, validates Marvell's Teralynx switching and AI fabrics under production-like conditions, and builds live demos for trade shows — carrying platform programs from concept to production.
What sets this team apart is breadth of ownership: engineers here aren't limited to validating a single switch chip — they work across the full breadth of Marvell's AI platform portfolio, from Teralynx network fabrics to integrated optical platforms like co-packaged optics (CPO) and near-packaged optics (NPO), as well as emerging memory fabric products such as CXL and photonic fabric memory appliances. This cross-domain ownership — networking, optics, and memory, together — is work that's often siloed across separate teams elsewhere, and the results feed directly into customer proof points and executive reviews.

What You Can Expect

Key Responsibilities

•     Own Teralynx-based switch configuration, SONiC deployment, and lab fabric design.

•     Design, tune, and validate RoCE-based AI fabrics for scale-up and scale-out workloads.

•      Architect rail-optimized, leaf-spine, fat-tree, and multi-plane fabric topologies for distributed training and inference clusters.

•     Configure and optimize ECN, PFC, UEC, congestion-control behavior, and telemetry pipelines.

•      Analyze GPU collective communication patterns (all-reduce, all-gather, broadcast, reduce-scatter) and their impact on east-west traffic and fabric topology design.

•     Work closely with hardware architecture and product management teams to validate and demonstrate features that accelerate AI workflows.

•     Debug packet loss, traffic hotspots, head-of-line blocking, and queue behavior using counters and fabric telemetry.

•      Build and extend network stress-test and fabric validation to support repeatable, automated benchmark runs.

•     Work closely with performance and systems teams to correlate network behavior with end-to-end workload outcomes.

•     Produce repeatable network configurations, fabric best practices, and validation artifacts for reuse in customer-facing solutions engagements.

What We're Looking For

Required Qualifications

•     Expertise in data center, backbone and internet protocols and technology

•     Experience with RoCE and AI-fabric tuning in high-performance computing or data center environments.

•      Working understanding of GPU collective communication patterns (all-reduce, all-gather, reduce-scatter) and how they shape network topology decisions.

•     Strong hands-on knowledge of switch automation, telemetry collection, and counter analysis.

•     Ability to diagnose congestion, latency, and traffic-path , control-plane and data-plane issues in distributed environments.

•     Experience with SONiC, SAI, network SDKs, and programmable switching platforms.

•      Familiarity with Ethernet-based AI fabrics and emerging AI interconnect architectures, including high-speed Ethernet (200G/400G/800G/1.6T).

•     Experience supporting benchmark labs, proofs of concept, or customer-facing infrastructure demonstrations.

•     Comfortable on the Linux command line, and have an understanding of the Linux networking stack and internals

•     Comfortable working across hardware and software integration boundaries.

•     Experience deploying telemetry for network fabrics using SNMP, gNMI, or gNXI. 

•      Experience building or extending network validation frameworks and stress-testing tools, not just operating them.

•     Familiarity with Ultra Ethernet Consortium (UEC) specifications and their implications for future fabric design.

Preferred Qualifications

•     Experience with containers and orchestration frameworks such as Kubernetes. 

•     Experience working with CI/CD pipelines to manage lab infrastructure.

Education and Certifications

•     Bachelor's or Master's degree in Computer Science, Computer Engineering, Network Engineering, or a related field; a PhD is a plus. 5- 15 years relevant experience.

•     Vendor network infrastructure certifications such as CCNP, CCIE, JNCIE, or equivalent are preferred.

Expected Base Pay Range (USD)

154,680 - 231,700, $ 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.

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