This role pays less than
91%
of similar roles. Most pay
$158,025–$235,750
— the shaded band above.
At the midpoint, this role pays about
$139k
versus about
$197k
for comparable roles.
Based on 240 similar postings.
Employer
About Qualcomm
Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.
Qualcomm currently has
623 open roles
on FindRole.
Listed pay typically runs
$148,300–$222,500
across 603 roles with salary data.
As a Senior Systems Engineer, Data Center AI, you will join the systems engineering team to research, develop, optimize, and validate software, hardware, architecture, algorithms, and machine learning solutions for cutting-edge AI datacenter technology. You will build AI/ML solutions that integrate hardware products and software ecosystems to provide high performance, power efficiency, and scalability. Your daily work involves designing and deploying Gen AI and LLM applications, implementing fine-tuning and distillation techniques, and performing model benchmarking and functional analysis. The role requires proficiency in Python and ML frameworks like PyTorch and TensorFlow, alongside a deep understanding of system performance profiling and parallel computing. You will address technical challenges involving inference performance across heterogeneous nodes, including CPUs, GPUs, and specialized accelerators, while ensuring all systems meet rigorous requirements and standards for data center and hybrid AI applications.
What does a Systems Engineer earn in California?
Median $194000 from 61 postings across 16 companies.
Develop AI/ML solutions integrating hardware, software, and ecosystem components for optimized inference performance and scalability.
Design, implement, and deploy Generative AI and Large Language Model (LLM) applications.
Implement fine-tuning and distillation techniques to improve model performance.
Research, design, and validate system-level software, hardware architectures, and deep learning algorithms.
Perform AI model benchmarking and functional analysis to define technical requirements and specifications.
Propose deployment strategies involving model optimization for high-performance inference.
Analyze system designs including interface definitions and performance metrics for new or modified systems.
Triage system-level problems to identify root causes and report debugging results to the team.
What we're looking for
A Bachelor's degree in Engineering, Information Systems, Computer Science, or a related field with 2+ years of experience is required.
A Master's degree in Engineering, Information Systems, Computer Science, or a related field with 1+ year of experience is required.
A PhD in Engineering, Information Systems, Computer Science, or a related field is accepted as an alternative qualification.
Candidates must have strong proficiency in Python and machine learning frameworks like PyTorch and TensorFlow.
Candidates must possess a deep understanding of ML development, deployment, applications, system performance profiling, and parallel computing.
Preferred experience includes working with GenAI architectures, fine-tuning models, reinforcement learning, and compiler optimizations for ML workloads.
Preferred skills include knowledge of microservices, distributed systems, and optimizing inference across heterogeneous nodes like CPUs and GPUs.
Proficiency in version control (Git), containerization (Docker, Kubernetes), and MLOps tools is preferred.
Python
AI
Machine Learning
scikit-learn
TensorFlow
PyTorch
SQL
Tableau
Power BI
Matplotlib
Plotly
GitHub Copilot
GAIAA
Data Analysis
Scripting
Agile
Scrum
Systems Engineering