AI Production Infrastructure Engineer

Warp Sciences; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Warp Sciences seeks an infrastructure and systems engineer to build the foundation for production AI workloads. You’ll work across core infrastructure, data systems, and the training and inference stack, ensuring reliability and scalability as AI pipelines scale in California.

You’ll optimize GPU utilization, data throughput, and cost, own production reliability, and collaborate with model teams to turn experiments into dependable production systems.

Qualifications

• Proven track record designing, shipping, and operating production systems at scale.
• Strong fundamentals in distributed systems, Linux, networking, storage, concurrency, and performance engineering.
• Experience building and operating cloud infrastructure, container orchestration, and infrastructure as code.
• Experience with data infrastructure, including distributed processing, pipelines, and storage systems.
• Hands-on experience supporting AI training and inference workloads, including GPU infrastructure and distributed execution.
• Ability to profile systems, identify bottlenecks, and deliver measurable improvements in performance, reliability, or cost.
• AI-native approach: collaborate with model teams and use AI tools in engineering workflows.
• Strong ownership, communication, and judgment when requirements are ambiguous.

Responsibilities

• Design, build, and operate infrastructure for large-scale AI training and inference.
• Build reliable data infrastructure for ingestion, processing, storage, and delivery to training/evaluation workflows.
• Optimize GPU utilization, distributed training performance, inference latency, throughput, and cost.
• Optimize data storage and access for training and inference.
• Own production reliability through observability, capacity planning, incident response, and recovery.
• Improve deployment, orchestration, and developer tooling for rapid progress.
• Collaborate with AI engineers/researchers to turn experiments into dependable production systems.
• Diagnose bottlenecks across compute, networking, storage, data pipelines, and model execution.

Skills

Distributed systems

Linux

Networking

Storage

Performance engineering

Observability

Incident response

Container orchestration

Infrastructure as code

AI workloads

GPU infrastructure

Tools

Cloud infrastructure

Kubernetes

Docker

Terraform

CI/CD pipelines

Monitoring tools

Job description

Warp Sciences seeks an infrastructure and systems engineer to build the foundation for production AI workloads. You’ll work across core infrastructure, data systems, and the training and inference stack, ensuring reliability and scalability as AI pipelines scale in California.

You’ll optimize GPU utilization, data throughput, and cost, own production reliability, and collaborate with model teams to turn experiments into dependable production systems.