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About the role
As a Machine Learning Systems Administrator / DevOps Engineer, you will maintain and build the core infrastructure behind both our model training and our inference cloud. You’ll split your time between our large-scale training clusters and the deployments and services that run our models in production on heterogeneous compute, which spans multiple accelerator types and vendors. You’ll work closely with our training, inference, and product teams to keep these systems reliable, secure, and fast. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Experience with Linux system administration, user and access management, and automation
- Demonstrated expertise in scripting languages for system tooling and automation (bash, Python, etc.)
- Experience with containers (Docker) and scheduling/orchestration systems (Slurm, Kubernetes)
- Experience running production services, including monitoring and alerting (e.g., Prometheus, Grafana), deployment pipelines, and incident response
- Experience building tooling for cluster validation and reliability (accelerator, networking, and storage health checks)
- Experience setting up and managing developer tools and third-party services (e.g., cloud storage providers, Docker Hub, Slack, Gmail, Telegraf, experiment trackers)
More about this role
As a Machine Learning Systems Administrator / DevOps Engineer, you will maintain and build the core infrastructure behind both our model training and our inference cloud. You’ll split your time between our large-scale training clusters and the deployments and services that run our models in production on heterogeneous compute, which spans multiple accelerator types and vendors. You’ll work closely with our training, inference, and product teams to keep these systems reliable, secure, and fast. You’ll also lay the groundwork for workflows that can scale with us.
Training clusters
Administration and automation of our Linux-based cluster environments
User onboarding/offboarding, access control, and security auditing
Monitoring system resources, job scheduling, and cluster health
Bring-up and validation of new hardware (compute, networking, storage)
Supporting and improving developer workflows (e.g., VSCode remote development, Docker)
Enabling and supporting large-scale training jobs
Inference cloud
Deploying, operating, and scaling inference services across heterogeneous accelerators
Managing container orchestration, CI/CD, and rollout pipelines for model deployments
Building...
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