Principal Engineer, Memory-Centric AI Compute Architect

Qualcomm

Quick summary

Work type: On-site
Location: Hillsboro, OR
Salary: $192,000–$288,000 / yr
Posted: 3 days ago
Closes: Dec 27, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $215k

This role $240k

$158k most similar roles pay here $302k

This role pays more than 71% of similar roles. Most pay $184,587–$246,150 — the shaded band above. At the midpoint, this role pays about $240k versus about $215k for comparable roles.

Based on 240 similar postings.

Employer

About Qualcomm

Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.

Qualcomm currently has 533 open roles on FindRole.

Listed pay typically runs $148,300–$222,500 across 513 roles with salary data.

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At a glance

TL;DR · Principal Engineer, Memory-Centric AI Compute Architect

Role Posting Log in to save

Join the Qualcomm Memory System/Technology Team in Process & Package Solutions Group as an experienced engineer or scientist to tackle AI workload mapping challenges for data center, mobile, and compute systems. You will develop innovative algorithms and architectures that enhance performance by minimizing data transfer bottlenecks between processors and memory, working closely with teams like AI architects, memory controllers, and SoC designers. Key responsibilities include architecting scalable AI/ML infrastructure, optimizing AI models for better performance and power efficiency, and co-optimizing compute, memory, and connect fabric allocations. Proficiency in Python, C, and knowledge of AI models such as LLMs, transformers, and CNNs is essential, alongside expertise in computer and memory architectures, dataflow protocols, and high-bandwidth memories like HBM. This role offers a unique opportunity to contribute to cutting-edge technologies that address critical performance bottlenecks in the rapidly evolving field of edge AI and connected computing.

Skills

Python C C++ CNN Transformer LLM RNN HBM Memory_Controller Dataflow Bus_Protocols Tensor_Cores Near_Memory_Computing Performance_Modeling_Tools 2.5D_3D_Integration DRAM Perl

What you'll do

Develop new algorithms for memory-centric compute systems to enhance AI workload performance.
Optimize AI models for better speed, power efficiency, and scalability in various devices.
Co-optimize allocation of compute, memory, and connect fabrics to improve system metrics.
Validate developed algorithms using programming languages like Python and C for optimization.
Use state-of-the-art modeling tools and numerical analysis techniques for performance evaluation.

What we're looking for

Experience in computer and memory architectures.
Knowledge of AI models including CNNs, transformers, LLMs, and multi-modal AI.
Understanding of dataflow, memory, and bus protocols.
Expertise in tensor cores, near-memory computing, and high-bandwidth memories like HBM.
Proficiency in performance modeling tools and numerical analysis techniques.
Master's or Ph.D. in Electrical Engineering, Computer Science, or related field.
Experience in developing and optimizing AI algorithms for memory-centric systems.

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