EngineeringHybrid

Senior/Staff AI Engineer

DDN · Hybrid

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Last seen by MeritLog September 12, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from DDN's Ashby job board and last checked it on September 12, 2026.

Source: the employer's Ashby job board. Open the original listing for current details.

Job details

Work model: Hybrid
Salary: Not listed by source
Location: Remote - California

Hiring context

How this role compares at DDN

DDN has 94 live roles in MeritLog’s catalog across 9 job families, and 55 of them are in engineering. 20 of those listings publish a pay range, a disclosure rate of 21%.

DDN concentrates this hiring in:

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What you'd 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
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
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.
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
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

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

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 -

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