Job description

## 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. 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. * Strong leadership abilities to motivate and guide development teams. * 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. * 10+ years of experience in High-Performance Computing System Engineering or Software with 5+ years in AI system optimization. * Master's or PhD in Computer Science or Electrical Engineering. Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries). To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications. EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification. Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law. Pay range and Other Compensation & Benefits: $234,800.00 - $352,200.00 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. If you would like more information about this role, please contact Qualcomm Careers.

Requirements

Must-haves

Qualcomm Technologies, Inc.
Engineering Group, Engineering Group > Machine Learning Engineering
Strong written and verbal communication skills are critical. In this cross functional role, excellent people and leadership skills are key.
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.
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.
PhD in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
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.
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.

Nice-to-haves

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.
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.

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