Etched.ai

Developer Experience Engineer

San Jose$150k–$275kfulltimemidAdded today

About this role

Etched seeks a Developer Experience Engineer to build automation, infrastructure, and observability systems for a hardware and software team focused on AI inference. You'll optimize job scheduling, CI/CD pipelines, cloud environments, and monitoring tools across chip design and model deployment workflows in a hybrid cloud and on-premises setup.

What you'll do

Develop automation tools and streamline development, testing, and deployment workflows
Optimize and manage Slurm-based job scheduling for AI workloads and chip design
Build observability solutions using Grafana, Prometheus, and OpenTelemetry
Manage containerized environments with Docker and Kubernetes for scalability
Enhance CI/CD pipelines using GitHub Actions, Jenkins, Buildkite, or Bazel
Integrate and manage cloud resources on AWS and GCP for hybrid workloads

What they're looking for

Python for automation and infrastructure development
Slurm job scheduling in HPC environments
Prometheus, Grafana, and OpenTelemetry monitoring
Docker and Kubernetes with Helm charts
CI/CD tools (GitHub Actions, Jenkins, Buildkite)
Infrastructure-as-code (Terraform, Ansible)
AWS or GCP cloud infrastructure
Data pipelining tools (Airflow, Prefect, Dagster)

Benefits

Full medical, dental, and vision coverage with generous premium support
$2,000/month housing subsidy for office-proximate residents
Daily lunch and dinner provided at office
Relocation support for San Jose area moves
Unlimited compute budget subject to ROI justification

Likely interview questions

Walk us through your experience managing Slurm job scheduling in an HPC environment. How have you optimized job throughput and resource utilization for compute-intensive workloads?
Describe a CI/CD pipeline you've built or significantly improved. What tools did you use, and how did you measure the impact on developer productivity?

Tell us about your experience with observability and monitoring. How have you used Prometheus, Grafana, or OpenTelemetry to diagnose and resolve infrastructure issues?
How have you approached containerization and orchestration challenges with Docker and Kubernetes? Have you worked with Helm charts or cluster scaling?
Describe your experience automating infrastructure with Python, Terraform, or Ansible. What was the most complex infrastructure-as-code project you've tackled?
Have you worked with AI/ML workflows, such as managing GPU-accelerated workloads or data pipelines? What tools and challenges did you encounter?
How do you balance supporting multiple teams with different needs? Tell us about a time you had to prioritize tooling improvements that benefited hardware, software, and research teams.

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