Machine Learning Infrastructure Engineer - Site Reliability

Job Description

Job Description

Zyphra is an artificial intelligence company based in Palo Alto, California.

As a Machine Learning Infrastructure Engineer - Site Reliability , 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)

• 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

• Experience in high-performance compute environments, such as ML clusters or GPU farms

• Background in infrastructure as code (e.g., Ansible, Terraform)

• Familiarity with software release engineering for ML/AI systems

• 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 (e.g., vLLM, Ray, SGLang)

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

• Familiarity with containers (Docker, Apptainer) and their integration with scheduling systems (Slurm, Kubernetes)

• Our research methodology is to make grounded, methodical steps toward 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

• Comprehensive medical, dental, vision, and FSA plans

• Competitive compensation and 401(k)

• Relocation and immigration support on a case-by-case basis

• On-site meals prepared by a dedicated culinary team; Thursday Happy Hours

• In-person team in Palo Alto, CA, with a collaborative, high-energy environment