Senior/Staff AI Engineer

🕒 May 15

🏄 California – Remote

🏄 California – Remote

💵 $150k - $250k / year

⏰ Full Time

🟠 Senior

🤖 AI Engineer

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DDN

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1998

🤖 Artificial Intelligence

💰 $10M Funding Round on 2011-06

Artificial Intelligence • Data Center and Cloud Computing • High Performance Computing

DDN is a global leader in AI data intelligence solutions, providing high-performance computing and sophisticated data management technologies. With a focus on accelerating AI deployments and advanced data analytics, DDN's products, including the Data Intelligence Platform and advanced storage systems, serve diverse sectors such as healthcare, financial services, and government. DDN is committed to transforming enterprise data infrastructure to leverage the full potential of AI and drive operational efficiency.

📋 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

🎯 Requirements

• 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.