Senior AI Infrastructure Engineer for LLMs

AItoolnavio; San Francisco (CA); On-site; USD 170,000 - 205,000; Full time

Benefits offered by this job

Health insurance

401(k) with match

Restricted Stock Units

Paid parental leave

Teladoc

Paid time off

Commuter benefits

Tuition reimbursement

HSAs/vision/dental

Life/disability insurance

Job summary

Crusoe is seeking a Senior Production Engineer to join our Production Engineering team in San Francisco. You will design and operate reliable managed AI services, focusing on serving and scaling LLM workloads, with a strong emphasis on reliability, performance, and cost efficiency.

You’ll own automation, observability, and SLIs/SLOs, collaborating across AI, platform, and infra teams to optimize large-scale training and inference clusters, and to resolve issues in distributed AI systems.

Qualifications

• Experience designing and implementing production-grade distributed systems.
• Hands-on with large language models and ML infrastructure.
• Ability to define and measure SLIs/SLOs for AI workloads.
• Strong programming skills in Python, Go, Java or C++.
• Familiarity with container orchestration (Kubernetes).

Responsibilities

• Design and operate reliable managed AI services for LLM workloads.
• Build automation and reliability tooling for AI pipelines and inference.
• Define and improve SLIs/SLOs to meet performance targets.
• Collaborate with AI, platform, and infra teams to optimize clusters.
• Develop observability through telemetry and performance tuning.
• Investigate reliability issues in distributed AI systems.
• Contribute to architecture of next-gen distributed systems for AI-first environments.

Skills

Distributed systems design

LLMs / AI infrastructure

SRE mindset

Python

Go

Java

C++

Kubernetes

Collaboration & communication

Job description

Crusoe is seeking a Senior Production Engineer to join our Production Engineering team in San Francisco. You will design and operate reliable managed AI services, focusing on serving and scaling LLM workloads, with a strong emphasis on reliability, performance, and cost efficiency.

You’ll own automation, observability, and SLIs/SLOs, collaborating across AI, platform, and infra teams to optimize large-scale training and inference clusters, and to resolve issues in distributed AI systems.