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

Qualcomm • San Diego, CA

24d • $234,800 - $352,200

About The Position

Qualcomm Technologies, Inc. is seeking an experienced Embedded ML SW Architect focused on Snapdragon Mobile products. As a senior member of the Mobile Handset software architecture and system design team, this role analyzes requirements and architects and develops high-level designs for new use cases and features. The architect drives technical initiatives to evolve Snapdragon Mobile AI software, including performance and power enhancements across software and hardware stacks and tools for state-of-the-art machine learning solutions.

The role collaborates with development leads, software and hardware architects, project engineers, product managers, customer engineers, OEMs, and external partners. It requires extensive experience in mobile software architecture and deployment, system software performance optimization, benchmarking, and performance analysis across CPU, GPU, and NPU. Familiarity with AI in areas such as computer vision, audio, and generative AI is relevant. Hands-on experience with agentic AI systems, LLMs/SLMs, and embedded ML on resource-constrained devices is a strong plus. Strong communication, people, and leadership skills are important.

The position develops architecture for AI-driven features and experiences, prototypes solutions, and helps shape product and technology roadmaps. It serves as a technical expert in solution development and commercialization, design reviews, partner alignment, and assessment of AI and mobile industry trends. The role architects on-device agentic experiences and runtime resource management, including context and memory management, perception pipelines, heterogeneous compute scheduling, and LLM/SLM serving under power, thermal, and memory constraints.

The posted pay range is $234,800.00–$352,200.00. Qualcomm also describes a competitive annual discretionary bonus program, the opportunity for annual RSU grants for eligible employees, and a competitive benefits package.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field and 8+ years of hardware engineering, software engineering, systems engineering, or related experience; or a master’s degree in one of these fields and 7+ years; or a PhD in one of these fields and 6+ years.
• The preferred qualifications also specify 10+ years of high-performance computing systems engineering or software experience, including 5+ years in AI systems optimization.
• Extensive experience with mobile software architecture and deployment, system software performance optimization, benchmarking, and performance breakdown analysis involving CPU, GPU, and NPU.
• Strong written and verbal communication skills, with the ability to explain complex technical concepts to technical and non-technical stakeholders.
• Strong people, leadership, and problem-solving skills; ability to guide development teams and work cross-functionally.
• Strong background in algorithm development and performance analysis using profiling tools, including modifying algorithms to improve performance.
• Strong software engineering principles and familiarity with software development methodologies, version control systems, and agile project management.
• Demonstrated ability to learn, think, and adapt in a fast-changing environment; detail-oriented analytical and debugging skills, strategic thinking, and ability to drive innovation.

Nice To Haves

• 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, and NPU and context/perception pipelines.
• Mobile platform experience; knowledge of Android frameworks and embedded system implementations.
• Excellent understanding of AI frameworks such as TensorFlow and PyTorch.
• Experience with LLMs/foundation models and SLMs, LoRA/adapter-based fine-tuning, and on-device personalization; MCP experience is a plus.
• Experience with agentic AI architectures, including planning, tool use/orchestration, context and memory management, and deployment on resource-constrained embedded devices.
• Experience architecting, designing, and implementing software frameworks for heterogeneous or multiprocessor systems, including runtime placement of ML/agentic workloads across CPU, GPU, and NPU.
• Experience designing graph-based (DAG) or state-machine-driven schedulers for heterogeneous multi-workload resource allocation with adaptation to live telemetry.
• Experience allocating resources among multiple concurrent AI workloads or agents competing for shared compute and memory.
• Background in feedback-driven scheduling, control systems, or adaptive and self-correcting systems.
• Experience with embedded ML system-level deployment, scheduling, and lifecycle management under tight memory, power, and thermal constraints.
• Experience with LLM/SLM serving internals, including KV-cache and model-state management, paging/eviction, and state sharing or reuse across sessions or agents.
• Understanding of complete AI software stacks and AI hardware acceleration technologies.
• Understanding of memory subsystems and interconnect bandwidth, including HBM/LPDDR, cache hierarchies, and NPU memory architecture, and experience minimizing redundant data movement.
• Experience modeling AI networks and workloads to estimate performance and power and translating estimates into optimizations.
• Experience with context and perception pipelines, including sensor fusion, screen/scene understanding, and multimodal context for personalized or agentic on-device experiences.
• Knowledge of state-of-the-art AI in one or more areas such as computer vision, audio, or generative AI.
• Proficiency in C, C++, and Python; familiarity with TypeScript, Java, or Kotlin is a plus. Ability to use agentic development tools is also a plus.
• Master’s or PhD in Computer Science or Electrical Engineering.

Responsibilities

• Develop architecture for complex AI-based features and experiences for Snapdragon Mobile products and recommend architecture enhancements.
• Research independently and work cross-functionally to define requirements; develop hands-on prototypes.
• Architect on-device agentic experiences, including context and memory management, perception pipelines such as screen or scene understanding and sensor fusion, and heterogeneous placement of LLM/SLM workloads across CPU, GPU, and NPU within power, thermal, and memory limits.
• Architect closed-loop, telemetry-driven resource scheduling for CPU, GPU, and NPU workloads, adapting placement to live power, thermal, and service-level signals.
• Design memory and model-state lifecycle management for on-device LLM/SLM serving, including KV-cache and context-state caching, eviction, paging, and reuse or sharing across sessions.
• Evaluate memory bandwidth and data-movement constraints, including high-bandwidth memory and on-chip interconnects, to improve AI throughput and efficiency on constrained silicon.
• Serve as a technical expert in developing and commercializing solutions; participate in design reviews and recommend improvements aligned with business goals and customer needs.
• Influence and negotiate with external partners to gain support for ideas and align on implementation plans and milestones.
• Monitor AI and mobile industry trends, competitors’ products, and engineering advances, and share expertise within or across teams.
• Contribute to discussions defining short- and long-term product and technology roadmaps.

Benefits

• Competitive annual discretionary bonus program.
• Opportunity for annual RSU grants; employees on sales-incentive plans are not eligible for the annual bonus.
• Competitive benefits package designed to support employees at work, at home, and at play.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal