Platform Engineer

As a Platform Engineer, you'll be responsible for designing and maintaining the systems that keep Zyphra's infrastructure robust, observable, secure, and scalable. Your work will be essential to ensuring the reliability and reproducibility of ML workloads, the safety and control of deployments, and the long-term maintainability of our compute environments.

Building and improving observability systems (monitoring, logging, alerting)

Managing Infrastructure as a Service across the stack along with CI/CD in close partnership with engineering teams

Designing resilient build and deployment systems across research and production environments

Implementing secure release processes with strong auditability and rollback support

Collaborating closely with ML engineers, DevOps, and infra teams to improve system reliability and performance

Leading incident response, root-cause analysis, and postmortems with a focus on learning and prevention

This role is ideal for someone who loves building systems that make other teams faster, safer, and more productive

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

Experience in high-performance compute environments, such as ML clusters or GPU farms as well as hyperscaler cloud environments (i.e. AWS, GCP, etc.)

Background in infrastructure as code (i.e., Terraform, Ansible, etc.)

Familiarity with containers (i.e., Docker, Apptainer) and their integration with scheduling systems (i.e., Kubernetes, Slurm)

Familiarity with software release engineering for ML/AI systems is a plus

Experience managing run-books, DRP, change management, and general fault tolerance

Experience with deployment strategies at scale

Experience designing reliable environments for experimental workloads and reproducible runs

Knowledge of compliance and audit standards in deployment and system security

Experience with load testing, fault injection, and chaos engineering to harden systems under stress

Passion for building tooling that makes infrastructure invisible and reliable for end users

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

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

Exposure to backend development for ML model serving (i.e., vLLM, Ray, SGLang, Triton)