DDN provides high-performance data intelligence and storage platforms that enable rapid, low-latency, high-throughput data access for NVIDIA GPU clusters, accelerating AI, LLM, GenAI, RAG, and HPC workloads.
WHAT YOU’LL DO - Build and optimize LLM serving and inference systems for production environments - Improve performance across GPU and CPU pathways - Work on KV cache, memory, storage, and throughput bottlenecks - Design and scale systems that support RAG and retrieval-heavy AI workloads - Contribute to infrastructure where storage architecture and systems efficiency materially affect AI performance - Solve engineering problems at the intersection of AI, high-performance systems, and distributed infrastructure WHAT WE’RE LOOKING FOR - An engineer who has spent meaningful time building or optimizing production AI systems, not just experimenting with models - Someone who understands how inference performance is shaped by the interaction between compute, memory, storage, and serving architecture - Deep hands-on experience working close to the systems layer — for example, improving how workloads run across GPU and CPU resources, reducing bottlenecks, or tuning infrastructure for better throughput and latency - Evidence of real ownership in areas like model serving, retrieval, caching, storage, or distributed performance, rather than purely application-layer AI work - The ability to move comfortably between architecture decisions and hands-on implementation, especially in environments where efficiency and scale matter - A background that suggests you can operate in technically demanding environments, whether that comes from AI infrastructure, high-performance systems, storage platforms, or adjacent distributed systems work - PhD preferred, but far less important than having built serious systems in the real world WHY THIS ROLE IS COMPELLING - This is not a “prompt engineering” job. - This is not an “AI wrapper” job. - This is not a generic backend role with AI sprinkled on top. - This is a chance to work on the infrastructure that determines whether modern AI systems are fast, scalable, efficient, and commercially viable. - If you want to work on the real mechanics of AI performance — serving, retrieval, compute efficiency, memory behavior, storage architecture, and inference at scale — this is where that work happens. WHO WILL LOVE THIS ROLE - Engineers who enjoy deep systems problems - Builders who care about performance, scale, and architecture - People who want to work where AI meets infrastructure - Candidates who would rather solve hard technical bottlenecks than ship surface-level AI features WHO SHOULD NOT APPLY This role is not for: - Purely academic researchers without meaningful production ownership - Generic software engineers without clear AI systems or inference depth - Candidates focused mainly on prompt engineering or lightweight application integrations - MLOps generalists who have not worked deeply on serving, storage, or performance-critical AI systems -