Senior HPC Systems Architect

At Lambda, we are at the forefront of accelerating artificial intelligence and machine learning capabilities through our groundbreaking compute infrastructure. Founded in 2012, our mission is to empower AI engineers and researchers by providing them with the most efficient and powerful tools to train, fine-tune, and deploy AI models at scale. We recognized early on the immense computational power required for deep learning and set out to build solutions that make this power accessible and affordable. Our innovative approach began with developing high-performance GPU-powered workstations and servers, meticulously designed for the rigorous demands of AI development. This hardware foundation was engineered for plug-and-play simplicity, enabling teams to rapidly deploy and begin their work without complex setup procedures.

Building on our hardware expertise, Lambda launched the Lambda GPU Cloud, a specialized cloud service offering on-demand and reserved access to cutting-edge NVIDIA GPUs. This platform is engineered to provide a seamless and scalable environment for both AI training and inference, catering to the needs of startups and large enterprises alike. A key component of our ecosystem is the Lambda Stack, a comprehensive AI software repository that simplifies the setup and management of popular deep learning frameworks and drivers with one-line installations. This focus on a streamlined software experience, combined with our high-performance hardware and cloud offerings, allows AI developers to transition from concept to production with unprecedented speed and efficiency. We are committed to pushing the boundaries of what's possible in AI by continuously innovating and providing the essential infrastructure that fuels the next generation of intelligent systems. Our solutions are trusted by leading research institutions, Fortune 500 companies, and pioneering AI-native organizations globally, as we help them tackle complex computational challenges and drive significant advancements in their respective fields.