Senior/Staff AI Engineer

Data Direct Networks; California (MO); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Vacation plans

Paid holidays

Bonus programs

Tuition reimbursement

Employee referral program

Excellent medical, dental and vision

Paid leave programs

Anniversary and recognition awards

Job summary

Data Direct Networks is seeking an engineer to build and optimize LLM serving and inference systems for production environments. You will work on performance across GPU and CPU pathways and tackle bottlenecks in memory, storage, and throughput.

You will design and scale systems to support RAG and retrieval-heavy AI workloads, contributing to infrastructure where storage architecture and system efficiency directly affect AI performance.

Qualifications

• Experience building production AI systems and serving workloads.
• Understanding of compute/memory/storage interactions on inference.
• Hands-on work near the systems layer with GPU/CPU tuning.
• Ownership across model serving, retrieval, caching, or distributed performance.

Responsibilities

• Build and optimize LLM serving/inference systems for production.
• Improve performance across GPU and CPU pathways.
• Scale systems for RAG and retrieval-heavy workloads.
• Address bottlenecks in memory, storage, and throughput.

Skills

AI systems

Model serving

Inference performance

System optimization

Distributed systems

Job description

We’re Looking for the Best and Brightest

We are the world’s leading data intelligence platform that reliably accelerates massive datasets for actionable real-time insights. Join our team to help the best and brightest minds tackle the world’s biggest challenges in business, science, medicine, academia and government.

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.

Employment Type

Full time

Location Type

On-site

Benefits

• Highly Competitive Vacation Plans
• Paid Holidays
• Bonus Programs
• Tuition Reimbursement
• Employee Referral Program
• Excellent Medical, Dental and Vision Benefits
• Paid Leave Programs
• Anniversary and Recognition Awards