Senior Systems Engineer, Data Center AI

Originally posted 8 October 2026 by the employer.

This role focuses on Qualcomm AI inference accelerators for data center and hybrid AI applications.

About the role

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 AI datacenter technology. This role focuses on Qualcomm AI inference accelerators for data center and hybrid AI applications.

What you'll do

• Develop AI/ML solutions that integrate Qualcomm AI hardware products, technologies, software, and ecosystem for 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/or 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 and deployment.

What you'll need

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering experience.
• 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.

Nice to have

• 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.
• 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.
• Experience with automation tools like GitOps, containerization technologies (Docker, Kubernetes), ML lifecycle management tools.
• Experience with rack-level orchestration tools and data center automation.
• Experience working in a large matrixed organization.

Skills: AI inference accelerators, Gen AI, LLM applications, AI hardware, deep learning algorithms