Qualcomm
Datacenter AI Systems and Solutions Engineer, Sr Staff
- Compensation
- Not disclosed
- Location
- San Diego, California, United States of America
- Work setup
- Onsite
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Solutions engineering
- Posted
Skills
Must have
- 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 provid
- 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 aut
- 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 ide
- 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
- Pay range and 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 compon
- If you would like more information about this role, please contact Qualcomm Careers.
Nice to have
- Master’s or PhD in Engineering, Computer Science, Information Systems, Physics, or a related discipline
- Strong proficiency in Python and experience with ML frameworks, APIs, REST services, and microservice-based architecture
- Hands-on experience designing, deploying, and operating AI/ML systems in production
- Solid understanding of Generative AI architectures, including transformers, diffusion models, and hybrid systems (LLMs, LVMs, embeddings)
- Experience with large-scale AI systems architecture, including microservices, distributed systems, event-driven designs, and fault-tolerant/resilient architectures
- Practical experience with AI inference serving, performance optimization, and scalability across heterogeneous hardware
- Experience with MLOps practices for AI application development, deployment, monitoring, and lifecycle management
- Familiarity with automation and DevOps tooling, including GitOps workflows, containerization (Docker), orchestration platforms (Kubernetes), and ML lifecycle tools
- Strong problem-solving skills with a customer and solution-focused mindset
- Experience with cluster schedulers and resource managers (e.g., Slurm, PBS) and workload orchestration is a plus
- Experience with observability, monitoring, and debugging tools for ML pipelines and inference services
- Proven ability to operate effectively in a large, matrixed organization, influencing across teams
- Experience with fine-tuning and optimization of GenAI models, including reinforcement learning techniques, is a plus
Full description
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Company:
Qualcomm Technologies, Inc.
Job Area:
Engineering Group, Engineering Group > Systems Engineering
General Summary:
As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all.
As a Qualcomm Datacenter AI Systems and Solutions Engineer, you will research, develop, optimize, and validate software, hardware, architecture, algorithms, and machine learning solutions that enable the deployment of cutting-edge AI datacenter technology.
Qualcomm Solution Engineers collaborate across functional teams to meet and exceed system-level requirements and standards. This is a great opportunity to innovate and develop leading-edge products and solutions around best-in-class Qualcomm AI inference accelerators for data center, and hybrid AI applications.
Preferred Qualifications:
- Master’s or PhD in Engineering, Computer Science, Information Systems, Physics, or a related discipline
- Strong proficiency in Python and experience with ML frameworks, APIs, REST services, and microservice-based architecture
- Hands-on experience designing, deploying, and operating AI/ML systems in production
- Solid understanding of Generative AI architectures, including transformers, diffusion models, and hybrid systems (LLMs, LVMs, embeddings)
- Experience with large-scale AI systems architecture, including microservices, distributed systems, event-driven designs, and fault-tolerant/resilient architectures
- Practical experience with AI inference serving, performance optimization, and scalability across heterogeneous hardware
- Experience with MLOps practices for AI application development, deployment, monitoring, and lifecycle management
- Familiarity with automation and DevOps tooling, including GitOps workflows, containerization (Docker), orchestration platforms (Kubernetes), and ML lifecycle tools
- Strong problem-solving skills with a customer and solution-focused mindset
- Experience with cluster schedulers and resource managers (e.g., Slurm, PBS) and workload orchestration is a plus
- Experience with observability, monitoring, and debugging tools for ML pipelines and inference services
- Proven ability to operate effectively in a large, matrixed organization, influencing across teams
- Experience with fine-tuning and optimization of GenAI models, including reinforcement learning techniques, is a plus
- Well versed in open-source development practices, collaboration, and code quality standards
- Exposure to rack-level orchestration, fleet management, and data center automation is a plus