Machine Learning Systems Administrator / DevOps Engineer: Training Clusters & Inference Cloud

Linuxconfig

San Francisco (CA)

On-site

USD 180,000 - 240,000

Full time

3 days ago

Be an early applicant

Get a reply from this employer

Your resume and cover letter, tailored to exactly what they’re hiring for.

Get past ATS filters

Benefits offered by this job

Comprehensive medical

401(k) plan

Relocation support

In-office snacks/meals

Unlimited PTO

In-person team in San Francisco

Job summary

Zyphra is seeking a Machine Learning Systems Administrator / DevOps Engineer to maintain and build the core infrastructure for model training and inference cloud. You’ll split time between large-scale training clusters and production deployments, ensuring reliability, security, and performance across heterogeneous accelerators.

You’ll own critical systems end to end, collaborate with training, inference, and product teams, and lay the groundwork for scalable workflows in a fast-moving AI

Qualifications

Experience with Linux system administration, user and access management, and automation.
Demonstrated scripting expertise in Bash, Python, or similar for tooling and automation.
Experience with containers (Docker) and scheduling/orchestration systems (Slurm, Kubernetes).
Experience running production services with monitoring/alerting (Prometheus, Grafana) and CI/CD pipelines.
Experience building tooling for cluster validation and reliability across accelerators, networking, and storage health checks.
Experience with dev tools and third-party services (cloud storage, Docker Hub, Slack, Gmail, Telegraf, experiment trackers).
Excellent debugging and troubleshooting skills across compute, storage, and networking.
Strong communication skills and collaboration across technical and non-technical teams.

Responsibilities

Administer and automate Linux-based cluster environments.
Onboard/offboard users, manage access control, and perform security audits.
Monitor resources, scheduling, and cluster health; validate new hardware.
Support and improve developer workflows (e.g., VSCode remote dev, Docker).
Deploy, operate, and scale inference services across heterogeneous accelerators.
Manage container orchestration, CI/CD, and rollout pipelines for model deployments.
Build observability with metrics, logging, alerting, and dashboards; own incident response.

Skills

Linux admin

Scripting (bash/python)

Docker

Kubernetes

Slurm

Prometheus

Grafana

Cluster tooling

Developer tooling

Networking

Communication

Tools

Apptainer

Terraform

Ansible

Job description

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)

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

Qualifications / Additional Skills:

Experience with infrastructure as code (e.g., Ansible, Terraform)

Prior work supporting ML/AI infrastructure, including GPU management and workload optimization

Experience deploying and operating LLM serving stacks (e.g., vLLM, SGLang, Ray)

Experience with multiple accelerator platforms (e.g., NVIDIA CUDA, AMD ROCm, TPUs)

Experience with high-performance networking (InfiniBand, RoCE) and distributed storage

Experience working with cloud platforms such as AWS, Azure, or GCP

Familiarity with Apptainer and with integrating containers into Slurm and Kubernetes

Why Work at Zyphra:

Our research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued