DDN
Senior/Staff AI Engineer
RemoteUnited States only
- Role
- AI / ML
- Experience
- Staff
- Employment
- Full-time
Salary not disclosed
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Open to US only. Set where you work from to check your eligibility.
No BS summary
Senior or Staff AI infrastructure engineer for production LLM serving and inference systems. Needs deep systems-layer experience with GPU/CPU performance, memory/storage bottlenecks, retrieval/RAG, caching, and distributed performance. California remote role.
Core skills
LLM servingInference systemsRAG
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
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 they require
- Meaningful time building or optimizing production AI systems, not just experimenting with models
- Understanding of 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, such as 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
- Ability to move comfortably between architecture decisions and hands-on implementation, especially in environments where efficiency and scale matter
- Background in technically demanding environments such as AI infrastructure, high-performance systems, storage platforms, or adjacent distributed systems work
- Preferred: PhD
- Interest in deep systems problems
- Interest in performance, scale, and architecture
- Interest in working where AI meets infrastructure
- Preference for solving hard technical bottlenecks over shipping surface-level AI features
- Not purely academic researchers without meaningful production ownership
- Not generic software engineers without clear AI systems or inference depth
- Not candidates focused mainly on prompt engineering or lightweight application integrations
- Not MLOps generalists who have not worked deeply on serving, storage, or performance-critical AI systems
Benefits
- Chance to work on infrastructure that determines whether modern AI systems are fast, scalable, efficient, and commercially viable
- Work on the real mechanics of AI performance: serving, retrieval, compute efficiency, memory behavior, storage architecture, and inference at scale
DDN is positioned as NVIDIA’s storage and data intelligence partner for AI factories and the NVIDIA AI Data Platform.
Data Storageddnet.org/
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