Etched.ai

Infrastructure Software Engineer

San Jose$150k–$250kfulltimemidAdded today

About this role

Etched seeks an Infrastructure Software Engineer to design and build the orchestration layers, observability stacks, and control planes powering a hybrid HPC cluster for AI ASIC development. You'll enable engineers to iterate faster on simulation, synthesis, and CI workflows while managing infrastructure at scale across on-premise and cloud environments.

What you'll do

Design and build orchestration layers for hybrid high-performance clusters supporting simulation, synthesis, and CI of AI ASICs
Develop a programmable infrastructure control plane ensuring reproducibility, auditability, and rapid iteration across the stack
Implement real-time telemetry and tracing systems to surface insights from millions of metrics and enable proactive debugging
Architect a full observability stack with dashboards, alerting, automated responses, and synthetic testing frameworks
Prototype workload orchestration and migration strategies between on-premise and cloud environments
Create tools and abstractions that simplify massive parallelism for engineers

What they're looking for

Systems design and software engineering
Kubernetes and SLURM orchestration
Infrastructure-as-code (Terraform, Ansible, Puppet)
Observability stack tools (Prometheus, Grafana, VictoriaMetrics)
Distributed systems and high-performance computing
Cloud and on-premise infrastructure management
EDA workflows and CI/CD pipelines
Networking and zero-trust architecture

Likely interview questions

Walk us through your experience designing and operating hybrid HPC clusters. How have you balanced performance, cost, and reliability across on-premise and cloud resources?
Describe a time you built observability or monitoring infrastructure from scratch. What telemetry, tracing, and alerting tools did you use, and how did it help teams detect and resolve issues?

Tell us about your experience with infrastructure-as-code tools like Terraform, Ansible, or Puppet. How do you ensure reproducibility and auditability in your infrastructure deployments?
What's your experience orchestrating workloads at scale? Have you worked with SLURM, Kubernetes, or similar systems? What challenges did you face?
Describe your approach to designing abstractions and tooling that enable other engineers to do complex work (like submitting parallel jobs or managing compute resources) without deep systems knowledge.
How have you approached real-time telemetry and insights from high-volume metrics? What tools or techniques have you used to prevent performance regressions?
Tell us about a time you had to solve a problem that required deep understanding of both software engineering practices and systems-level infrastructure concerns. What was the outcome?

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