Platform Engineer (AI)

Location: San Francisco, United States; on-site, with an in-person team.

Requirements

• Experience with high-performance compute environments, including ML clusters, GPU farms, and hyperscaler clouds such as AWS or GCP.
• Infrastructure as code experience with tools such as Terraform or Ansible.
• Experience with containers such as Docker or Apptainer and scheduling systems such as Kubernetes or Slurm.
• Experience with deployment strategies at scale, disaster recovery planning, change management, fault tolerance, and reliable experimental environments.
• Knowledge of compliance and audit standards for deployment and system security.
• Experience with load testing, fault injection, and chaos engineering.

Nice to have

• Experience supporting ML/AI infrastructure, including GPU management and workload optimization.
• Exposure to backend development for ML model serving with tools such as vLLM, Ray, SGLang, or Triton.
• Experience with software release engineering for ML/AI systems.

What you will do

• Build and improve observability systems for monitoring, logging, and alerting.
• Manage infrastructure as a service and CI/CD in partnership with engineering teams.
• Design resilient build and deployment systems across research and production environments.
• Implement secure, auditable release processes with rollback support.
• Collaborate with ML, DevOps, and infrastructure teams to improve reliability and performance.
• Lead incident response, root-cause analysis, and postmortems focused on prevention.

Culture & Benefits

• Research and engineering work follows methodical, step-by-step approaches to ambitious goals.
• New ideas are encouraged, with willingness to make significant bets on promising concepts.
• Medical, dental, vision, and FSA plans.
• Competitive compensation, 401(k) plan, unlimited PTO, and company holidays.
• Relocation and immigration support may be available on a case-by-case basis.
• In-office snacks and meals in a collaborative San Francisco environment.