Embedded ML Staff Engineer -- Snapdragon Mobile

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General Summary:

Seeking an experienced Embedded ML Staff Engineer focused on Snapdragon Mobile products. As a senior individual contributor on the team working on Mobile Handset software architecture and system design and development, you will analyze requirements and design/implement solutions for new use-cases and features. You will drive technical initiatives to evolve Snapdragon Mobile AI software, helping deliver performance and power enhancements into the SW/HW stacks and tools of state-of-the-art machine learning solutions. You will work closely with architects, development leads, software and hardware engineers, project engineers, and product managers.

Ideal candidate has strong experience with Mobile SW design 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 important. Strong collaboration and mentorship skills are a plus.

Minimum Qualifications:

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

• Design and implement architecture for complex AI features and experiences within your subsystem for Snapdragon Mobile products; propose architecture improvements.

• Research and collaborate cross-functionally to help define requirements; build hands-on prototypes.

• Design and implement on-device agentic experience components, 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.

• Implement closed-loop, telemetry-driven resource scheduling components for heterogeneous compute (CPU/GPU/NPU), re-evaluating 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/sharing across sessions under tight device memory budgets.

• Evaluate and help 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 resource in design reviews; recommend improvements aligned to 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 various engineering fields; share expertise within the team.

• Contribute technical input and trend analysis to roadmap discussions led by architecture and product leadership.

Preferred Qualifications

• Excellent understanding of AI frameworks (e.g., TensorFlow, PyTorch). Experience with large language models/foundational models and small language models (SLMs), including LoRA/adapter-based fine-tuning and on-device personalization techniques, MCP's a plus.

• Experience with agentic AI architectures -- planning, tool-use/orchestration, context and memory management -- and their deployment on resource-constrained embedded devices.

• Experience in architecting, designing, and implementing SW frameworks for complex heterogeneous or multi-processor 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 with resource allocation across multiple concurrent AI workloads/agents competing for shared compute and memory, not just single-model placement.

• Background in closed-loop or control-systems thinking (feedback-driven scheduling, adaptive/self-correcting systems) is a plus.

• Experience with embedded ML at the system level -- model deployment, scheduling, and lifecycle management under tight memory, power, and thermal constraints on mobile/embedded platforms.

• Experience with LLM/SLM serving internals -- KV-cache and model-state management, paging/eviction strategies, and state sharing or reuse across concurrent sessions or agents.

• Good Understanding of complete AI Software stack and familiarity with AI hardware acceleration technologies.

• Understanding of memory subsystem and interconnect bandwidth (e.g., HBM/LPDDR, cache hierarchies, NPU memory architecture) and how bandwidth -- not just compute -- bottlenecks AI inference performance; experience architecting to minimize redundant data movement.

• Experience in modeling AI networks and workloads to extract performance and power estimates, and converting that into optimization.

• Experience with context and perception pipelines (sensor fusion, screen/scene understanding, multimodal context) that power personalized or agentic on-device experiences.

• Strong background in algorithm development, performance analysis using profiling tools, and algorithmic modification methods for performance improvement.

• Knowledge of state of the art in AI for one or more domains such as Computer Vision, Audio, Generative AI.

• Knowledge of Mobile platforms, Android framework, Embedded system implementations.

• Strong software engineering principles.

• Proficiency in programming languages such as C, C++, Python. Understanding Typescript, Java, Kotlin is a plus. The ability to use agentic tooling for development is a plus.

• Excellent communication skills to articulate complex technical concepts to non-technical and technical stakeholders.

• Experience mentoring junior and mid-level engineers; ability to influence technical direction without formal authority.

• Detail-oriented with 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.

• 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 in Computer Science or Electrical Engineering.

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Pay rangeand Other Compensation & Benefits :

The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer - and you can review more details about our US benefits at this link.

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