Remote Staff Infrastructure Engineer - AI/ML Platform

Descript; San Francisco (CA); Remote; USD 220,000 - 292,000; Full time

Benefits offered by this job

Healthcare package

401k matching

Catered lunches

Flexible vacation time

Job summary

Descript is hiring a Staff Infrastructure Engineer to own the foundation your team runs on—platform, AI training/inference pipelines, and security boundaries. You will work across GCP, Kubernetes, and GPU fleets, balancing cost with reliability while leading architectural decisions and mentoring other engineers.

Remote within the US with some in-person collaboration at times. The role emphasizes ownership, outcomes over specs, and a path toward senior leadership in platform/engineering at

Qualifications

• 8+ years building and operating production distributed systems with infrastructure focus.
• Experience with AI-enabled platforms and scalable deployments.
• Strong reliability, monitoring, and incident-management approach.

Responsibilities

• Own our platform: GCP, Kubernetes, Temporal, GPU fleet, and deployment tooling.
• Manage AI enablement substrate: training/inference pipelines and cost.
• Make cost a design constraint and enable scalable growth.
• Security responsibilities: IAM, secrets, least-privilege, and supply-chain integrity.
• Produce readable infrastructure: IaC, runbooks, and in-repo context.
• Improve team learning and shipping: hypotheses, instrumentation, incremental releases.
• Provide architectural direction, mentoring, and clear written guidance.

Skills

ML/AI infrastructure

Platform engineering

Security engineering

Tools

Kubernetes

GCP

CI/CD

Job description

Descript is hiring a Staff Infrastructure Engineer to own the foundation your team runs on—platform, AI training/inference pipelines, and security boundaries. You will work across GCP, Kubernetes, and GPU fleets, balancing cost with reliability while leading architectural decisions and mentoring other engineers.

Remote within the US with some in-person collaboration at times. The role emphasizes ownership, outcomes over specs, and a path toward senior leadership in platform/engineering at