Senior/Staff AI Engineer

📋 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
• Solve engineering problems at the intersection of AI, high-performance systems, and distributed

🎯 Requirements

• An engineer who has spent meaningful time building or optimizing production AI systems, not just
• Understand how inference performance is shaped by compute, memory, storage, and serving architecture
• Hands-on experience near the systems layer—improving workloads across GPU/CPU, reducing
• Real ownership in areas like model serving, retrieval, caching, storage, or distributed performance
• Ability to move between architecture decisions and hands-on implementation for efficiency and scale
• Background in AI infrastructure, high-performance systems, storage platforms, or related

🎁 Benefits

• Not a prompt engineering role
• Not an AI wrapper or generic backend with AI sprinkled on top
• Opportunity to work on infrastructure that determines AI systems’ speed, scalability, efficiency
• Work on real mechanics of AI performance—serving, retrieval, compute efficiency, memory behavior
• Be at the intersection of AI and infrastructure for scalable AI systems
• Team focuses on deep systems problems and performance