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.
🏢 About Zyphra
Our research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued. We strongly value new and crazy ideas and are very willing to bet big on new ideas. We move as quickly as we can; we aim to minimize the bar to impact as low as possible. We all enjoy what we do and love discussing AI.
🎯 The Role
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.
✅ Key Responsibilities
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
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
📌 Required Qualifications
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)
Excellent debugging and troubleshooting skills across compute, storage, and networking
Strong communication skills and ability to collaborate across technical and non-technical teams
⭐ Desirable Experience
Experience with infrastructure as code (e.g., Ansible, Terraform)
Prior work supporting ML/AI infrastructure, including GPU management and workload optimization
