NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology and amazing people. Today, we're harnessing the boundless possibilities of AI to build the next era of computing. An era in which our GPU acts as the brain of computers, robots, and self-driving cars that can understand the world. Accomplishing unprecedented goals calls for imagination, inventiveness, and exceptional talent from around the world. As a NVIDIAN, you'll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Join our team and discover how you can build a lasting impact on the world.
NVIDIA's Silicon Co-Design Group (SCG) leads the full product development lifecycle, from early architecture definition through silicon bringup to product release. The ArchDev team is the hub for silicon and system-level feature development, driving tradeoff analysis, system integration, and POR alignment across the entire organization. This is where ideas become chips, and chips become products that define the state of the art — and we're building that future with some of the most motivated engineers in the industry. We're looking for a Senior Memory Systems Engineer to own HBM and LPDDR integration in sophisticated SoCs. This role covers the full stack, including silicon, package, embedded software, testing, and product development. The engineer will resolve the toughest system-level memory challenges throughout the process, building solutions that hold up at scale.
What you'll be doing:
HBM & LPDDR System Integration and Bringup: Drive HBM and LPDDR system integration, bringup, characterization, and debug for next-generation SoCs — taking memory subsystems through the full arc from first silicon to production-ready at scale.
Full-Stack Memory Closure: Own memory performance, power management, thermal, and reliability closure across silicon, package, board, and firmware — translating characterization results into signed-off operating points and release criteria that the full program depends on.
Post-Silicon Margining, VF Shmoo & Correlation: Lead post-silicon margining, VF shmoo, eye, and correlation work across voltage, temperature, and frequency — building the characterization foundation that underpins every product decision downstream.
Memory Debug: Training, Calibration, SI/PI & Stability: Debug and resolve memory training, calibration, SI/PI, and system-level stability issues — the class of problems that sit at the intersection of electrical, physical, and software behavior and demand deep cross-domain expertise to resolve at scale.
Validation & Characterization Planning: Define validation, characterization, and issue-tracking plans across chip programs — building the framework that ensures the right tests exist, the right data gets collected, and issues are tracked with enough fidelity to close.
What we need to see:
BS or MS in EE/CE — or equivalent experience.
12+ years in HBM, LPDDR, or high-speed memory systems, with hands-on depth in silicon bringup, characterization, and debug at scale. PHY controller development experience is a significant plus and will set you apart.
Deep command of SI/PI, timing, margining, and memory training behavior — and the multi-functional fluency to work across design, package, firmware, validation, and product teams without losing the thread.
HBM PHY and controller architecture familiarity is useful context, but what this role actually demands is rarer: the system-level instinct to see where memory behavior is about to become a product problem — and the inventiveness to resolve it before it does.
Ways to stand out from the crowd:
AI-Accelerated Debug & Root Cause Analysis: Use LLM-assisted tools and ML models to pattern-match failure signatures across VF shmoo, eye diagrams, and margining datasets — converging on root cause and outlier detection faster than manual triage, with engineering judgment setting the bar for what the model can decide.
Experience with HBM3/HBM3E specifics, specific JEDEC margins, thermal-electrical co-sim, ECC methodology & power management is highly desired.
Intelligent Test & Validation Automation: Automate data collection, test sequencing, and results analysis across characterization programs — cutting cycle time from silicon arrival to product-ready data and freeing bandwidth for problems that require human expertise.
LLM-Assisted Architecture & Trade-Off Exploration: Accelerate architecture exploration, spec navigation, and cross-domain trade-off analysis using LLMs — surfacing precedents from prior programs and building characterization plans for expert review.
Memory is where performance lives or dies. At the power densities and bandwidths NVIDIA's next-generation SoCs demand, memory systems engineering is one of the most consequential disciplines on the chip. The engineer in this role will have direct influence over whether products meet their performance, power, and reliability targets — and will resolve those challenges alongside some of the most motivated memory and silicon teams in the industry. We're building an encouraging environment where inventiveness is expected, and impact is real. If this is the level you want to operate at, we'd like to hear from you.
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 196,000 USD - 310,500 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- BS or MS in Electrical Engineering or Computer Engineering, or equivalent experience
- 12+ years in HBM, LPDDR, or high-speed memory systems with hands-on silicon bringup, characterization, and debug at scale
- Deep command of SI/PI, timing, margining, and memory training behavior
- Multi-functional fluency to work across design, package, firmware, validation, and product teams
- PHY controller development experience
- Familiarity with HBM PHY and controller architecture
- Experience with HBM3/HBM3E specifics, JEDEC margins, thermal-electrical co-sim, ECC methodology, and power management
- Experience using LLM-assisted tools or ML models for debug, root-cause analysis, or test automation
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”









