Embedded ML SW Architect -- Snapdragon Mobile (Principal or Director level)

Company

Qualcomm Technologies, Inc.

Job Area

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary

Seeking an experienced Embedded ML SW Architect focused on Snapdragon Mobile products. As a senior member of the team working on Mobile Handset software architecture and software system design, you will have the opportunity to analyze requirements and architect/develop high level designs for new Use-cases and features. You will also drive technical initiatives to evolve Snapdragon Mobile AI software, helping to drive performance and power enhancements into the SW and HW stacks and SW tools of state-of-the-art machine learning solutions on the Snapdragon Mobile platform.

You will work closely with development leads, software and hardware architects, project engineers, product managers, customer engineers, OEMs, and external partners.

Ideal candidate has extensive experience with Mobile SW architecture and deployment, system SW performance optimization, benchmarking, and performance breakdown analysis with CPU, GPU, NPU, and knowledge of state of the art in AI for one or more of the domains such as Computer Vision, Audio, Generative AI. Hands-on experience with agentic AI systems, LLMs/SLMs, and embedded ML at the system level on resource-constrained devices, including heterogeneous compute placement across CPU/GPU/NPU and context/perception pipelines that feed those experiences, is a strong plus. Experience with Mobile Platforms is highly desirable.

Strong written and verbal communication skills are critical. In this cross functional role, excellent people and leadership skills are key.

Minimum Qualifications

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

OR

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

OR

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

Responsibilities

Develop architecture for complex features and experiences built around AI for Snapdragon Mobile products; recommend architecture enhancements.

Independently research and work cross-functionally to define requirements. Hands on prototype development.

Architect on-device agentic experiences, including context and memory management, perception (e.g., screen/scene understanding, sensor fusion), and heterogeneous compute placement for LLM/SLM workloads across CPU, GPU, and NPU under tight power, thermal, and memory budgets.

Architect closed-loop, telemetry-driven resource scheduling for heterogeneous compute (CPU/GPU/NPU) -- continuously re-evaluating and re-placing workloads against live power, thermal, and SLA signals rather than deciding placement once statically.

Design memory and model-state lifecycle management for on-device LLM/SLM serving -- including KV-cache and context-state caching, eviction, paging, and reuse/sharing across sessions under tight device memory budgets.

Evaluate and architect around memory bandwidth and data-movement constraints (e.g., high-bandwidth memory, on-chip interconnects) to maximize achievable AI throughput and efficiency on constrained silicon.

Serve as a technical expert in developing and commercializing solutions; participate in design reviews and recommend improvements to ensure alignment with business goals and customer needs.

Influence and negotiate with external partners to gain buy-in for ideas and align on project and subsystem implementation plans and milestones to ensure the needs of all stakeholders are met.

Act as a technical expert on AI and Mobile industry trends, competitors' products, and advances in various engineering fields; share expertise within and/or across teams.

Leverages technical expertise to identify trends and define future solutions by contributing to discussions on the short - and long-term strategic direction of the product/technology roadmaps.