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.

$200,000 - $500,000 a year

Compensation & Benefits

At Velaura, we believe exceptional talent deserves exceptional rewards. Compensation for this role includes a competitive base salary, performance-based incentives, and equity participation, allowing team members to share in the company's long-term success.

Your base pay will depend on your skills, qualifications, experience, and location.

In addition to compensation, Velaura offers a comprehensive benefits package that may include medical, dental, and vision coverage; paid time off; flexible work arrangements; professional development opportunities; and other benefits designed to support the well-being and growth of our team.

Velaura is committed to pay equity and transparency and regularly benchmarks compensation to ensure we remain competitive in the market.