Staff Machine Learning Engineer

Job Summary

Owning the infrastructure that powers AI, the full-time Staff Machine Learning Systems Engineer will design, build, and operate production systems for AI workloads, focusing on Kubernetes, CI/CD pipelines, and observability in a remote setting.

Key responsibilities

• Own and scale the AI compute and deployment platform, including Kubernetes operations and GitOps-based deployment pipelines
• Build and maintain inference and model-serving infrastructure, ensuring reliable serving patterns for LLM-powered workflows
• Manage observability and tracing systems, defining SLOs and incident response for AI infrastructure reliability

Required qualifications

• 8+ years of experience in infrastructure, platform, DevOps, or SRE engineering, with at least 3 years focused on ML/AI systems in production
• Deep experience with Kubernetes and cloud-native ecosystem tools, including autoscaling and GitOps
• Strong infrastructure-as-code skills, particularly with Terraform, and experience in secure cloud architecture design
• Proficiency in Python with experience in building production infrastructure tooling and observability pipelines
• Experience operating LLM-based systems in production, including inference routing and reliability patterns