Note: The job is a remote job and is open to candidates in USA. DDN is a company known for its high-performance infrastructure, and they are seeking a Senior/Staff AI Engineer to build the AI infrastructure layer that supports production models. This role focuses on optimizing systems for AI performance, including storage and distributed systems, ensuring efficiency and scalability in real-world applications.

Responsibilities

• 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

Skills

• 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

Company Overview

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