Senior Systems Engineer, Data Center AI

Technology, Data & Digital · Software & Web Development · Software Engineering · Data Science · Artificial Intelligence

In short

Qualcomm is seeking a Senior Systems Engineer for Data Center AI in San Diego, CA. This role involves researching, developing, and optimizing AI/ML solutions, hardware, and software for cutting-edge AI datacenter technology. You will collaborate across teams to meet system-level requirements and drive innovative AI inference accelerators.

Responsibilities

Develop AI/ML solutions integrating Qualcomm AI hardware, technologies, software, and ecosystem for optimal inference performance, power efficiency, and scalability.
Assist in designing, developing, implementing, and deploying Generative AI and LLM applications.
Contribute to implementing fine-tuning and distillation techniques.
Apply Systems knowledge to research, design, develop, simulate, and validate systems-level software, AI hardware, architecture, deep learning algorithms, and AI solutions.
Perform AI model benchmarking and functional analysis to drive requirements and specifications.
Propose deployment strategies with AI model/workload optimization.
Build AI solutions and develop/analyze system-level designs, including requirements, interface definitions, and functional/performance definitions.
Collaborate with internal teams to complete project work, including implementing and testing features and verifying AI system accuracy.
Stay updated on the latest advancements in AI/ML (models, HW/SW) and drive innovative solutions.
Develop new and innovative ideas for product or feature areas.
Drive system-level problem triage to determine root causes and present testing and debugging results.

Requirements

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.
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.

Desired Qualifications

Master's or PhD Degree in Engineering, Information Systems, 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, distributed systems).
Optimize inference performance across heterogeneous nodes (CPUs, GPUs, 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.

Benefits

Competitive annual discretionary bonus program.
Opportunity for annual RSU grants.
Highly competitive benefits package designed to support success at work, at home, and at play.
Details about US benefits available at a link.

Skills

PythonPyTorchTensorFlowGit

#Data Center#AI#Systems Engineering#Machine Learning#Deep Learning#Gen AI#LLM#PyTorch#TensorFlow#Python#Hardware#Software#Architecture#Algorithms#Performance Optimization#Benchmarking#Deployment#Optimization#Scalability#Power Efficiency#Parallel Computing#Transformers#Diffusion#Embeddings#Fine-tuning#Distillation#Reinforcement Learning#Compiler Optimizations#Microservices#Distributed Systems#Heterogeneous Computing#Caching#Batching#Hardware Acceleration#Version Control#Git#Gerrit#GitHub#GitLab#Open-source#MLOps#Docker#Kubernetes#GitOps#Orchestration Tools#Data Center Automation