AI Systems Architect (Models & Hardware Co-Design)

Role Overview

We are looking for an AI Systems Architect who sits at the intersection of machine learning models and hardware architecture. In this role, you will work across model development, algorithms, and hardware architecture to identify and shape the AI workloads that will define the next generation of compute platforms. You will evaluate emerging model architectures, understand their training and inference characteristics, and help translate them into efficient hardware implementations.

This role requires someone who is comfortable moving between mathematical models, software frameworks, and hardware architecture, and who enjoys solving problems at the boundary between disciplines.

Responsibilities

• ● Analyze modern AI model architectures including transformers and emerging alternatives to understand their computational and system requirements.
• ● Evaluate model suitability for different application domains and help determine which model architectures are best matched to specific workloads.
• ● Work closely with hardware architects to translate model requirements into efficient silicon implementations.
• ● Identify opportunities to modify or optimize models, algorithms, or dataflows to improve performance, efficiency, or scalability in hardware.
• ● Understand training and inference pipelines for modern AI models and identify implications for hardware design.
• ● Develop performance and efficiency models to guide architectural decisions.
• ● Collaborate with software and hardware teams to ensure that models can be deployed efficiently on new compute platforms.
• ● Stay current with emerging AI model research and identify opportunities where new architectures may benefit from specialized hardware.
• Required Qualifications
• ● Strong understanding of modern machine learning architectures, including transformers and related model families.
• ● Solid grounding in the mathematical foundations of machine learning, optimization, and deep learning algorithms.
• ● Experience working with ML frameworks such as 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, andhardware.

Preferred Qualifications

• ● Experience with hardware–software co-design or AI accelerator architecture.
• ● Familiarity with model optimization techniques such as quantization, sparsity, pruning, or distillation.
• ● Experience implementing or optimizing AI models for specialized hardware platforms.
• ● Exposure to emerging AI model architectures beyond transformers (e.g., world models, continuous-time networks, reinforcement learning systems).
• ● Experience working with robotics, autonomous systems, or embodied AI applications.
• Why Velaura?
• Velaura is building next-generation compute technology for cloud, edge, and Physical AI. Our solutions will enable robots, autonomous systems, drones, and other intelligent machines to operate efficiently in the physical world.

This is an opportunity to help build foundational technology at a time when the industry is undergoing fundamental change. You will work alongside experienced leaders, architects, engineers, and operators who have delivered industry-defining products across mobile, cloud, semiconductor, and AI platforms. If you enjoy solving difficult problems, working across disciplines, and helping shape the future of Physical AI, we would love to hear from you.

Equal Employment Opportunity and Accommodations

Velaura is committed to working with qualified individuals with physical or mental disabilities. Applicants who would like to contact us regarding the accessibility of our website or who need special assistance or a reasonable accommodation for any part of the application or hiring process may contact us at: careers@velaura.ai. This contact information is for accommodation requests only. Evaluation of requests for reasonable accommodation will be determined on a case-by-case basis.