# Systems Engineer, Data Center AI

About the Role

As a Datacenter AI Systems Engineer at Qualcomm, you will research, develop, optimize, and validate software, hardware, architecture, algorithms, and machine learning solutions for cutting-edge AI datacenter technology. You will collaborate across functional teams to meet system-level requirements and standards, innovating and developing leading-edge products around Qualcomm AI inference accelerators for data center and hybrid AI applications.

Key Responsibilities

• Develop AI/ML solutions that integrate Qualcomm AI hardware, technologies, software, and ecosystem to achieve 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 to implementing fine-tuning and distillation techniques.
• Apply systems knowledge to research, design, develop, simulate, and validate system-level software, AI hardware, architecture, deep learning algorithms, and AI solutions, 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/analyze system-level designs, including requirements, interface definitions, functional/performance definitions, and implementation of new or modified systems.
• Collaborate with own and other teams to complete project work, including implementing and testing features and verifying the accuracy of AI systems.
• Stay abreast of the latest advancements in the AI/ML space (models, HW/SW) and drive innovative solutions.
• Develop new and innovative ideas for product or feature areas.
• Drive triage of problems at the system level to determine root cause and present testing and debugging results.

Requirements & Tech Stack

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field.
• Strong proficiency in Python.
• Proficiency with ML frameworks.
• Deep understanding of ML development, deployment, and applications.
• Deep understanding of system performance profiling and parallel computing.
• Proficiency with version control systems (Git) and code review tools (Gerrit, GitHub, GitLab) and collaborative development workflows.
• Well-versed with open-source development practices.

Qualifications

• Master's or PhD Degree in Engineering, Computer Science, Physics, or related field.
• Good understanding of GenAI architectures (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: microservices, and distributed systems.
• Optimize inference performance across heterogeneous nodes (CPUs, GPUs, and specialized accelerators).
• Experience in implementing caching, batching, and parallelization strategies for high-throughput systems.
• Familiarity with hardware acceleration.
• Understanding of MLOps for AI application development and deployments is a plus.
• Experience with rack-level orchestration tools and data center automation is a plus.
• Experience working in a large matrixed organization.

Compensation

• Pay Range: $99,500.00 - $149,300.00
• Competitive annual discretionary bonus program.
• Opportunity for annual RSU grants.
• Competitive benefits package.