AI Systems Architect
Velaura AI develops ultra-low-power silicon and software technologies for AI compute infrastructure.
Maintainer signals as of 8/28/2026
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
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Analyze modern AI model architectures, evaluate computational and system requirements, and map them to specialized hardware. Translate model requirements into efficient silicon implementations, optimize models and dataflows, develop performance and efficiency models, and collaborate across software, algorithms, and hardware disciplines.
Requirements
- Strong understanding of modern machine learning architectures, including transformers.
- Strong mathematical foundation in machine learning, optimization, and deep learning algorithms.
- Experience with PyTorch, JAX, or TensorFlow.
- Ability to analyze model computation graphs and translate them into efficient dataflows and compute patterns.
- Experience with AI model training and inference workflows.
- Strong systems thinking and ability to work across software, algorithms, and hardware.
- Experience with hardware-software co-design or AI accelerator architecture.
- Familiarity with quantization, sparsity, pruning, or distillation.
- Experience optimizing AI models for specialized hardware platforms.
- Exposure to emerging AI model architectures beyond transformers.
- Experience with robotics, autonomous systems, or embodied AI applications.
Responsibilities
- Analyze modern AI model architectures, including transformers and emerging alternatives.
- Evaluate model suitability for different application domains and workloads.
- Translate model requirements into efficient silicon implementations.
- Optimize models, algorithms, and dataflows for performance, efficiency, and scalability.
- Analyze training and inference pipelines and their implications for hardware design.
- Develop performance and efficiency models to guide architectural decisions.
- Collaborate with software and hardware teams to deploy models efficiently on new compute platforms.
- Stay current with emerging AI model research.
Benefits
- Performance-based incentives
- Equity participation
- Medical coverage
- Dental coverage
- Vision coverage
- Paid time off
- Flexible work arrangements
- Professional development opportunities