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

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 clustersAdministration and automation of our Linux-based cluster environmentsUser onboarding/offboarding, access control, and security auditingMonitoring system resources, job scheduling, and cluster healthBring-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 jobsInference cloudDeploying, operating, and scaling inference services across heterogeneous acceleratorsManaging container orchestration, CI/CD, and rollout pipelines for model deploymentsBuilding observability for production services, including metrics, logging, alerting, and dashboardsOwning service reliability: uptime, incident response, and postmortemsCapacity planning, autoscaling, and utilization/cost tracking across hardware typesSecuring production systems through secrets management, network policy, and tenant isolationYou’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 automationDemonstrated 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 responseExperience 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 networkingStrong communication skills and ability to collaborate across technical and non-technical teamsQualifications / Additional Skills:Experience with infrastructure as code (e.g., Ansible, Terraform)Prior work supporting ML/AI infrastructure, including GPU management and workload optimizationExperience 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 storageExperience working with cloud platforms such as AWS, Azure, or GCPFamiliarity with Apptainer and with integrating containers into Slurm and KubernetesWhy 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 valuedWe strongly value new and crazy ideas and are very willing to bet big on new ideasWe move as quickly as we can; we aim to minimize the bar to impact as low as possibleWe all enjoy what we do and love discussing AIBenefits and Perks:Comprehensive medical, dental, vision, and FSA plansCompetitive compensation and 401(k) planRelocation and immigration support on a case-by-case basisIn-office snacks and meals providedUnlimited PTO and company holidaysIn-person team in San Francisco with a collaborative, high-energy environment