Senior/Staff AI Engineer

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    "description": "<h2>Overview</h2>\n<p>Build the AI infrastructure layer that determines whether modern models actually work in production.</p>\n<p>Most AI roles sit at the application layer. This one does not.</p>\n<p> </p>\n<p>At DDN, we’re hiring an AI Engineer to work on the hard part of AI: the systems, storage, and performance infrastructure behind real-world model serving and inference. This is the role for engineers who care about what happens under load, at scale, and in production — not just in demos.</p>\n<p> </p>\n<p>If your background sits at the intersection of AI infrastructure, distributed systems, and performance engineering, this is the kind of role where your depth will matter.</p>\n<h2>Job Description</h2>\n<p>What you’ll do</p>\n<ul>\n <li>Build and optimize LLM serving and inference systems for production environments</li>\n <li>Improve performance across GPU and CPU pathways</li>\n <li>Work on KV cache, memory, storage, and throughput bottlenecks</li>\n <li>Design and scale systems that support RAG and retrieval-heavy AI workloads</li>\n <li>Contribute to infrastructure where storage architecture and systems efficiency materially affect AI performance</li>\n <li>Solve engineering problems at the intersection of AI, high-performance systems, and distributed infrastructure</li>\n</ul>\n<p>What we’re looking for</p>\n<ul>\n <li>An engineer who has spent meaningful time building or optimizing production AI systems, not just experimenting with models</li>\n <li>Someone who understands how inference performance is shaped by the interaction between compute, memory, storage, and serving architecture</li>\n <li>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</li>\n <li>Evidence of real ownership in areas like model serving, retrieval, caching, storage, or distributed performance, rather than purely application-layer AI work</li>\n <li>The ability to move comfortably between architecture decisions and hands-on implementation, especially in environments where efficiency and scale matter</li>\n <li>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</li>\n <li>PhD preferred, but far less important than having built serious systems in the real world</li>\n</ul>\n<p>Why this role is compelling</p>\n<ul>\n <li>This is not a “prompt engineering” job.</li>\n <li>This is not an “AI wrapper” job.</li>\n <li>This is not a generic backend role with AI sprinkled on top.</li>\n <li>This is a chance to work on the infrastructure that determines whether modern AI systems are fast, scalable, efficient, and commercially viable.</li>\n <li>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.</li>\n</ul>\n<p>Who will love this role</p>\n<ul>\n <li>Engineers who enjoy deep systems problems</li>\n <li>Builders who care about performance, scale, and architecture</li>\n <li>People who want to work where AI meets infrastructure</li>\n <li>Candidates who would rather solve hard technical bottlenecks than ship surface-level AI features</li>\n</ul>\n<p>Who should not apply</p>\n<p>This role is not for:</p>\n<ul>\n <li>Purely academic researchers without meaningful production ownership</li>\n <li>Generic software engineers without clear AI systems or inference depth</li>\n <li>Candidates focused mainly on prompt engineering or lightweight application integrations</li>\n <li>MLOps generalists who have not worked deeply on serving, storage, or performance-critical AI systems</li>\n</ul>\n<p>Salary Range: $150,000 - $250,000</p>\n<h2>DDN</h2>\n<p>Why DDN - DDN has deep credibility in high-performance infrastructure, and this role sits in a part of the market where that foundation matters. If you want to build the systems serious AI depends on — rather than the layer that merely sits on top of it — this is a rare opportunity to do exactly that.</p>\n<p> </p>\n<p>Apply if you want to build the infrastructure behind production AI — not just consume it.</p>\n<p> </p>\n<p>#Linkedin</p>",
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