Top Tech Jobs & Startup Jobs in Charlotte, NC

Design and operate multicloud production infrastructure using AWS, GCP, and Azure. Manage Kubernetes clusters and implement Infrastructure as Code with Terraform, supporting AI systems for Fortune 500 clients.

Lead the design and implementation of ML infrastructure, working on the model lifecycle, collaborating with researchers and engineering teams, and ensuring robust AI systems.

The CUDA Kernel Engineer will develop and optimize NVIDIA CUDA kernels, profile GPU workloads, and contribute to GPU architecture decisions for AI systems.

The Staff Machine Learning Engineer will define and build ML capabilities, design production systems, and lead ML strategy in a high-growth AI cybersecurity company.

The role involves defining and building ML capabilities, creating production systems, and managing data strategy while collaborating with engineers and leading an ML team.

The VP AI/Machine Learning will define and implement machine learning capabilities in a high-growth cybersecurity company, focusing on strategy, infrastructure, and building production systems. The role involves defining ML applications, data strategy, and mentoring an ML team.

The role involves defining and building the company's machine learning capabilities, designing production ML systems, owning data strategy, and partnering with engineers. Requires experience in ML system production and software engineering fundamentals.

Define ML strategy and build production ML systems end-to-end (data pipelines, training, evaluation, inference). Own data strategy, integrate ML with product/backend, write production-quality code, and recruit and mentor the ML team while setting technical direction.

Lead and build the companys ML function: define strategy, design production ML systems and infrastructure, own data and evaluation practices, write production code, integrate ML into products, and recruit and mentor an expanding ML team.

Lead definition and implementation of the companys ML strategy and infrastructure. Build end-to-end production ML systems (data pipelines, training, evaluation, serving), own data strategy and feedback loops, integrate ML with product/backend, write production-quality code, and recruit/mentor the ML team as it scales.