What you’ll do
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Build and optimize LLM serving and inference systems for production environments
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Improve performance across GPU and CPU pathways
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Work on KV cache, memory, storage, and throughput bottlenecks
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Design and scale systems that support RAG and retrieval-heavy AI workloads
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Contribute to infrastructure where storage architecture and systems efficiency materially affect AI performance
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Solve engineering problems at the intersection of AI, high-performance systems, and distributed infrastructure
What we’re looking for
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An engineer who has spent meaningful time building or optimizing production AI systems, not just experimenting with models
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Someone who understands how inference performance is shaped by the interaction between compute, memory, storage, and serving architecture
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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
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Evidence of real ownership in areas like model serving, retrieval, caching, storage, or distributed performance, rather than purely application-layer AI work
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The ability to move comfortably between architecture decisions and hands-on implementation, especially in environments where efficiency and scale matter
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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
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PhD preferred, but far less important than having built serious systems in the real world
Why this role is compelling
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This is not a “prompt engineering” job.
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This is not an “AI wrapper” job.
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This is not a generic backend role with AI sprinkled on top.
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This is a chance to work on the infrastructure that determines whether modern AI systems are fast, scalable, efficient, and commercially viable.
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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
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Engineers who enjoy deep systems problems
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Builders who care about performance, scale, and architecture
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People who want to work where AI meets infrastructure
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Candidates who would rather solve hard technical bottlenecks than ship surface-level AI features
Who should not apply
This role is not for:
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Purely academic researchers without meaningful production ownership
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Generic software engineers without clear AI systems or inference depth
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Candidates focused mainly on prompt engineering or lightweight application integrations
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MLOps generalists who have not worked deeply on serving, storage, or performance-critical AI systems
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