Engineer: AI Infrastructure Optimization
Job Description
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.
Qualifications
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.
Role Appeal
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.
Ideal Candidates
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.
Not For
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.