Performance Modeling Architect – AI Systems
Velaura AI develops ultra-low-power silicon and software technologies for AI compute infrastructure.
Maintainer signals as of 8/29/2026
Funding history
About Velaura AI, Inc.
Velaura AI is an AI compute infrastructure company developing ultra-low-power silicon and software technologies. Its Titan Core™ platform uses proprietary digital chip IP, low-voltage libraries, EDA flows, and design methodologies to improve performance per watt in AI accelerators while maintaining performance, yield, and reliability. The company also applies this technology to Physical AI systems, including industrial robots, autonomous machines, drones, humanoids, and edge devices. Velaura engages hyperscalers, XPU companies, and developers of next-generation AI infrastructure and Physical AI solutions.
Skills
About the Role
Develop system-level performance models and simulation frameworks for AI workloads and compute architectures, modeling SoC components, software runtime behavior, memory systems, and interconnects to analyze bottlenecks, evaluate tradeoffs, and guide hardware and system architecture decisions.
Requirements
- Strong background in computer architecture, system architecture, or performance modeling.
- Experience building simulation or analytical models of complex hardware systems.
- Strong programming skills in Python, C++, or similar languages.
- Understanding of CPUs, GPUs, or AI accelerators.
- Ability to analyze complex systems and identify performance bottlenecks.
- Strong quantitative reasoning and ability to translate models into architectural insights.
- Experience modeling AI workloads or machine learning systems.
- Familiarity with architectural simulators or performance modeling tools.
- Experience with memory systems, interconnect architectures, or accelerator design.
- Exposure to hardware-software co-design methodologies.
- Experience modeling performance across software runtimes and hardware platforms.
Responsibilities
- Develop system-level performance models for AI workloads and compute architectures.
- Build simulation frameworks that capture interactions between AI models, software runtimes, and hardware systems.
- Model key SoC components, including compute units, memory hierarchies, and interconnects.
- Incorporate software and runtime behavior into modeling frameworks.
- Analyze system bottlenecks and evaluate architectural tradeoffs.
- Guide microarchitectural and system-level design decisions.
- Collaborate with software and ML teams to incorporate realistic workloads.
- Evaluate performance, latency, throughput, and energy efficiency.
Benefits
- Performance-based incentives
- Equity participation
- Medical coverage
- Dental coverage
- Vision coverage
- Paid time off
- Flexible work arrangements
- Professional development opportunities