Company

Qualcomm India Private Limited

Job Area

Engineering Group, Engineering Group > Software Engineering

General Summary

At Qualcomm, you will join a global leader in semiconductor technology and help shape the future of AI at the edge. You will work alongside world-class engineers, architects, and product leaders to define the software architecture that enables AI experiences across a diverse range of products, including wearables, smartphones, XR devices, automotive platforms, edge computing systems, and AI PCs.

The Core Platform Software Architecture team is seeking an experienced and highly influential Principal Engineer, AI Platform Software Architecture to drive the software and system architecture for next-generation AI inferencing platforms. In this role, you will lead cross-functional architectural initiatives spanning software, hardware, security, memory systems, and AI accelerators while helping shape the long-term technology strategy for Qualcomm's AI platform roadmap.

Minimum Qualifications

Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Software Engineering or related work experience.

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Software Engineering or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Software Engineering or related work experience.

4+ years of work experience with Programming Language such as C, C++, Java, Python, etc.

The successful candidate will serve as a technical leader responsible for influencing software and silicon architecture decisions across multiple product lines and future generations of AI platforms.

Key Responsibilities

Define and drive the long-term platform software architecture and technical strategy for AI inferencing across Qualcomm's portfolio of edge AI products, influencing technology investments and architectural decisions across multiple product generations.
Lead cross-functional architectural initiatives involving software, hardware, AI accelerator, security, and product teams to deliver scalable and differentiated AI platform solutions.
Collaborate with product management, customers, and silicon architecture teams to understand emerging AI workloads, product requirements, and technology trends, and translate them into platform architecture and roadmap recommendations.
Drive hardware/software co-design and influence future platform capabilities through architectural analysis, technical proposals, and strategic technology planning.
Lead system-level architecture and engineering efforts spanning operating systems, AI runtimes, memory systems, virtualization, security, interconnects, and heterogeneous compute architectures.
Evaluate architectural alternatives, perform tradeoff analysis, and develop architecture proposals and specifications that balance performance, power, scalability, security, and product requirements.
Drive alignment across organizations through architecture reviews, technical leadership, and effective stakeholder engagement.
Mentor and influence engineers and architects across the company while helping establish architectural best practices, technical standards, and long-term technology direction.

Preferred Qualifications

15+ years of experience architecting complex embedded, platform, or systems software solutions.
Demonstrated success driving architecture and technical strategy across multiple product generations and organizations.
Strong experience in hardware/software interface design, system architecture, and end-to-end platform engineering.
Deep expertise in several of the following areas:
SoC and computer architecture
ARM and RISC-V architectures
Linux kernel, operating systems, and device drivers
Memory management and memory subsystem architecture
DMA, IOMMU, and system interconnect technologies
Virtualization and hypervisor technologies
Security architecture, trusted execution environments, attestation, confidential computing, and model protection
AI inferencing runtimes, frameworks, compilers, and software stacks
Large Language Models (LLMs), Transformer architectures, and modern AI workloads
Heterogeneous computing systems involving CPUs, GPUs, DSPs, NPUs, and dedicated AI accelerators
Proven ability to influence senior technical leaders and drive consensus across geographically distributed teams.
Excellent communication, leadership, and technical presentation skills.

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