Embedded ML Staff Engineer -- Snapdragon Mobile

Qualcomm • San Diego, CA

21d • $223,600 - $335,400

About The Position

Qualcomm Technologies, Inc. is seeking an experienced Embedded ML Staff Engineer focused on Snapdragon Mobile products. As a senior individual contributor, you will work on mobile handset software architecture, system design, and development, analyzing requirements and designing and implementing solutions for new use cases and features. You will drive technical initiatives to evolve Snapdragon Mobile AI software and deliver performance and power improvements across software and hardware stacks and tools for state-of-the-art machine-learning solutions. The role works closely with architects, development leads, software and hardware engineers, project engineers, and product managers.

The position focuses on complex on-device AI features and agentic experiences for resource-constrained mobile devices. This includes context and memory management, perception pipelines, heterogeneous compute placement across CPU, GPU, and NPU, telemetry-driven scheduling, LLM/SLM serving state and memory lifecycle management, and optimization for memory bandwidth and data movement. The engineer also contributes to design reviews, partner implementation plans, and product and architecture roadmap discussions.

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 work experience; or a master's degree in one of these fields and 7+ years of relevant experience; or a PhD in one of these fields and 6+ years of relevant experience.
• 5–8 years of experience in High-Performance Computing System Engineering or Software, with 2+ years in AI system optimization.
• Bachelor's or master's degree in Computer Science or Electrical Engineering.
• Strong experience with mobile software design and deployment, system software performance optimization, benchmarking, and performance breakdown analysis across CPU, GPU, and NPU.
• Knowledge of state-of-the-art AI in one or more domains such as Computer Vision, Audio, or Generative AI.
• Strong software engineering principles and proficiency in programming languages such as C, C++, and Python.
• Strong written and verbal communication skills to articulate complex technical concepts to technical and non-technical stakeholders.
• Strong problem-solving, analytical, and debugging skills, with the ability to think strategically and drive innovative solutions.
• Demonstrated ability to learn, think, and adapt in a fast-changing environment.
• Familiarity with software development methodologies, version control systems, and agile project management practices.

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 and context/perception pipelines.
• Experience with mobile platforms.
• Excellent understanding of AI frameworks such as TensorFlow and PyTorch.
• Experience with large language or foundational models and small language models, including LoRA/adapter-based fine-tuning and on-device personalization; MCP experience is a plus.
• Experience with agentic AI architectures, including planning, tool use or orchestration, and context and memory management, and deploying them on resource-constrained embedded devices.
• Experience architecting, designing, and implementing software frameworks for complex 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 multi-workload resource allocation across heterogeneous compute, with closed-loop adaptation to live system telemetry.
• Experience allocating resources across multiple concurrent AI workloads or agents competing for shared compute and memory.
• Background in closed-loop or control-systems thinking, including feedback-driven scheduling and adaptive or self-correcting systems.
• Experience with embedded ML at the system level, including model deployment, scheduling, and lifecycle management under tight memory, power, and thermal constraints on mobile or embedded platforms.
• Experience with LLM/SLM serving internals, including KV-cache and model-state management, paging and eviction strategies, and state sharing or reuse across concurrent sessions or agents.
• Good understanding of the complete AI software stack and familiarity with AI hardware acceleration technologies.
• Understanding of memory subsystems and interconnect bandwidth, such as HBM/LPDDR, cache hierarchies, and NPU memory architecture, and how bandwidth can bottleneck AI inference performance; experience architecting to minimize redundant data movement.
• Experience modeling AI networks and workloads to estimate performance and power and translating those estimates into optimization.
• Experience with context and perception pipelines, including sensor fusion, screen/scene understanding, and multimodal context, for personalized or agentic on-device experiences.
• Strong background in algorithm development, profiling-based performance analysis, and algorithmic modification for performance improvement.
• Knowledge of mobile platforms, Android framework, and embedded system implementations.
• Understanding of TypeScript, Java, or Kotlin; ability to use agentic tooling for development.
• Experience mentoring junior and mid-level engineers and influencing technical direction without formal authority.

Responsibilities

• Design and implement architecture for complex AI features and experiences within the subsystem for Snapdragon Mobile products, and propose architecture improvements.
• Research and collaborate cross-functionally to help define requirements and build hands-on prototypes.
• Design and implement on-device agentic experience components, including context and memory management, perception such as screen or scene understanding and sensor fusion, and heterogeneous compute placement for LLM/SLM workloads across CPU, GPU, and NPU within power, thermal, and memory budgets.
• Implement closed-loop, telemetry-driven resource scheduling for heterogeneous compute, reevaluating workload placement against live power, thermal, and SLA signals.
• Implement 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 under device memory constraints.
• Evaluate and help architect around memory bandwidth and data-movement constraints, including high-bandwidth memory and on-chip interconnects, to maximize AI throughput and efficiency on constrained silicon.
• Serve as a technical resource in design reviews and recommend improvements aligned with business and customer needs.
• Collaborate with external partners on subsystem implementation plans and milestones.
• Stay current on AI and mobile industry trends, competitors' products, and advances in engineering, and share expertise within the team.
• Contribute technical input and trend analysis to roadmap discussions led by architecture and product leadership.

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: Senior