Top Tech Jobs & Startup Jobs in Charlotte, NC

Design, implement, and operate low-level components of Azure Storage distributed file systems. Define requirements, produce designs, write and reuse high-quality code, break down work, estimate tasks, and act as an on-call DRI to monitor and restore services. Drive reliability, performance, observability, and scalability improvements.

Design, build, and maintain Azure-based full-stack skilling platforms and CI/CD pipelines. Implement scalable microservices, REST APIs, automation, and AI/LLM features, improve site reliability, automate testing, and collaborate across teams to ensure secure, high-quality production deployments.

Drive consultative sales and account orchestration for prioritized accounts: identify, qualify, and close Microsoft Cloud consulting opportunities; build close plans; coordinate cross-functional account teams; manage contracts and transitions to delivery; maintain customer relationships and account plans to drive consumption and customer satisfaction.

Lead and scale a multidisciplinary product design organization to define end-to-end experience strategy. Hire, mentor, and grow designers and researchers; drive inclusive, user-centered design, AI-enabled prototyping, research strategy, cross-functional alignment, design systems, and measurable product impact. Oversee delivery, operations, and executive communication to achieve global skilling goals.

Design, curate, and evaluate large-scale text datasets for LLM pretraining. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; and collaborate with Safety, Ethics, and Governance to ensure dataset quality, privacy, and responsible AI alignment.

Design, implement, and optimize distributed pre-training infrastructure for large-scale GPU clusters. Profile, benchmark, and debug compute, memory, networking, and storage bottlenecks; optimize communication libraries and GPU programming stacks; collaborate with hardware and model teams to scale training, ensure reliability, and influence architecture and roadmap.

Lead end-to-end data science and AI engagements: design, develop, deploy and evaluate models, mentor teams, partner with stakeholders, drive adoption, and apply Responsible AI to deliver measurable business impact.

Build and optimize inference systems and tools for generative AI models, collaborate with researchers to improve performance, profiling, debugging, and deploy large-scale distributed inference solutions.

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Design and build evaluation infrastructure for generative AI on large GPU clusters. Develop and tune pretraining software, benchmark NVIDIA and AMD GPU clusters, collaborate with model scientists, implement evaluation methods and inference strategies, and inform compute roadmap and architectural decisions to support Copilot and consumer AI products.