GPU System Performance Architect

Jobtailor; California (MO); On-site; USD 180,000 - 260,000; Full time

Job summary

Jobtailor is seeking a senior expert to develop GPU-accelerated cloud computing architectures for large-scale data centers. You will lead performance modeling, data analysis, and visualization to optimize system efficiency across ongoing GPU-driven cloud projects.

The role involves cross-functional collaboration with software, product, and business teams to guide hardware-software interactions in virtualized environments, leveraging extensive research and industry standards.

Qualifications

• Masters or PhD in Computer Science, Electrical Engineering, or Computer Engineering, or equivalent experience.
• 10 years of relevant work or research experience.
• Excellent mathematical and analytical skills.
• Deep knowledge of computer architecture.
• Strong communication, organizational and interpersonal skills.
• Experience with analytical performance modeling, simulation, profiling and analysis.
• Background in data center/cloud computing design with experience crafting hardware for a virtualized environment.
• Expertise in data analysis and visualization.
• Familiarity with GPU computing and parallel programming models.
• Experience in performance modeling, characterization and optimization of PCIe and IO-centric workloads.

Responsibilities

• Develop GPU-accelerated cloud computing architectures with strong analytical performance modeling and data visualization.
• Collaborate across teams to optimize system performance and efficiency in large-scale data center environments.

Skills

10 years of experience

Analytical skills

Computer architecture

GPU computing

Data center design

Data analysis

Data visualization

Parallel programming models

PCIe/IO optimization

Cost and efficiency analysis

Education

Master's or PhD in CS/EE/CE or equivalent experience

Job description

• Develop innovative processor and system architectures for GPU-accelerated cloud computing
• Analyze trade-offs in system performance, cost and efficiency using analytical models, simulators and data visualization tools
• Understand and analyze hardware and software interactions in large-scale data center deployments of GPU-accelerated systems
• Collaborate across the company with software, product and business teams to guide GPU-accelerated cloud computing

Requirements

• Masters or PhD in a relevant discipline such as Computer Science, Electrical Engineering, or Computer Engineering, or equivalent experience
• 10 years of relevant work or research experience
• Excellent mathematical and analytical skills
• Deep knowledge of computer architecture
• Strong communication, organizational and interpersonal skills
• Experience with analytical performance modeling, simulation, profiling and analysis
• Background in data center/cloud computing design with experience crafting hardware for a virtualized environment
• Expertise in data analysis and visualization
• Familiarity with GPU computing and parallel programming models
• Experience in performance modeling, characterization and optimization of PCIe and IO-centric workloads

Core Competencies

Demonstrates expertise in developing GPU-accelerated cloud computing architectures, with strong analytical skills for performance modeling and data visualization. Collaborates effectively across teams to optimize system performance and efficiency in large-scale data center environments.

Highest-signal resume keywords

• GPU-Accelerated Cloud Computing
• Performance Modeling and Analysis
• Data Center Design
• Computer Architecture
• Data Analysis and Visualization

Hard Skills

• Analytical Performance Modeling
• Simulation and Profiling
• Parallel Programming Models
• Optimization of PCIe Workloads
• Data Visualization Tools
• Mathematical Analysis
• System Architecture Development
• Hardware and Software Interaction Analysis
• Cost and Efficiency Trade-Off Analysis
• Virtualized Environment Design

Soft Skills

• Strong Communication Skills
• Organizational Skills
• Interpersonal Skills

Certifications & Qualifications

• Masters or PhD in Computer Science
• Masters or PhD in Electrical Engineering
• Masters or PhD in Computer Engineering

Industry Keywords

• Cloud Computing
• Data Center
• GPU Computing
• Hardware Design
• Virtualization