Systems Engineer, Data Center AI

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 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 Systems 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. 

Minimum Qualifications:

•    Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.
OR
Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience.
OR
PhD in Engineering, Information Systems, Computer Science, or related field.

Principal Duties and Responsibilities:

• Develop AI/ML solutions that bring together Qualcomm AI hardware products, technologies, software, and ecosystem and provide best-in-class AI inference performance, power efficiency and scalability
• Assist in the design, development, implementation and deployment of Gen AI and LLM applications
• Contribute towards implementing fine tuning and distillation techniques
• Apply Systems knowledge and experience to research, design, develop, simulate, and/or validate systems-level software, AI hardware, architecture, deep learning algorithms, and AI solutions while ensuring system-level requirements and standards are met
• Perform AI model benchmarking and functional analysis to drive requirements and specifications
• Propose deployment strategies with AI model/workload optimization and deployment 
• Build AI solutions and develop and analyze system level design including requirements, interface definition, functional/performance definition, and implementation of a new system or modification of an existing system
• Collaborate with own team and other teams to complete project work, including implementing and testing features and verifying the accuracy of AI systems
• Keep abreast with the latest advancements in the AI/ML space (models, HW/SW) and drive innovative solutions.
• Develops new and innovative ideas for a product or feature area
• Drives triage of problems at the system level to determine root cause and presents results of testing and debugging to team members.

Required Skills:

• Strong proficiency in Python, and ML frameworks (PyTorch, TensorFlow).
• Deep understanding of ML development, deployment and applications.
• Deep understanding of system performance profiling and parallel computing.

Preferred Qualifications:

• Master's or PhD Degree in Engineering, Information Systems, Computer Science, Physics or related field
• Good understanding of GenAI architectures from transformers, diffusion, hybrid - LLMs, LVMs, embeddings
• Working experience with fine-tuning GenAI models and Reinforcement Learning
• Background in compiler optimizations for ML workloads is a plus
• Experience with architectural Patterns for Large-Scale AI Systems: Knowledge of microservices, and distributed systems
• Optimize inference performance across heterogenous nodes CPUs, GPUs, and specialized accelerators
• Experience in implementing caching, batching, and parallelization strategies for high-throughput systems.
• Familiarity with hardware acceleration
• Proficiency with version control systems (Git) and code review tools (Gerrit, GitHub, GitLab) and collaborative development workflows
• Well versed with open-source development practices
• Understanding of MLOps for AI application development and deployments is a plus
• Experience with automation tools like GitOps, containerization technologies (Docker, Kubernetes), ML lifecycle management tools is a plus
• Experience with rack-level orchestration tools and data center automation is a plus
• Experience working in a large matrixed organization

Minimum Qualificatio