Senior/Staff AI Engineer

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 -