Machine Learning Systems Administrator

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. You’ll also lay the groundwork for workflows that can scale with us.

You’ll Work Across:

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 observability for production services, including metrics, logging, alerting, and dashboards

• Owning service reliability: uptime, incident response, and postmortems

• Capacity planning, autoscaling, and utilization/cost tracking across hardware types

• Securing production systems through secrets management, network policy, and tenant isolation

You’ll own critical systems end to end. Your work will directly affect developer productivity, training throughput, and the performance and reliability of our inference platform.

What We’re Looking For / Requirements:

• 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)