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
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
Benefits and Perks:
Comprehensive medical, dental, vision, and FSA plans
Competitive compensation and 401(k) plan
Relocation and immigration support on a case-by-case basis
In-office snacks and meals provided
Unlimited PTO and company holidays
In-person team in San Francisco with a collaborative, high-energy environment