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