Senior ML Infra Engineer: GitOps, Secure GPU Kubernetes

ContentBuffer; New York (NY); On-site; USD 110,000 - 160,000; Full time

Benefits offered by this job

Healthcare (dental & vision)

401k

Pre-tax commuter benefits

Flexible work schedule

Parental leave

Paid vacation time

Company-wide events

Job summary

Schrödinger is hiring a Senior Machine Learning Infrastructure Engineer in New York to build and maintain the systems that run ML models and AI agents for drug discovery. You will own the infrastructure across our clusters and customer clouds on GCP and AWS, focusing on repeatable deployments, strong isolation, and cost-efficient GPU workloads.

You will work closely with ML engineers and scientists to design secure, scalable environments, implement GitOps-driven workflows, and ensure reliable,

Qualifications

• At least six years of infrastructure, platform, or SRE experience.
• In-depth understanding of Kubernetes operation (not just deployment).
• Robust background with GCP (and some familiarity with AWS).

Responsibilities

• Turn customer installs and upgrades into versioned, GitOps-driven releases (Helm, OpenTofu, Argo CD) with automated verification.
• Design isolation for multi-tenant agent sandboxes: network policy, workload identity, runtime sandboxing, and egress control.
• Secure CI/CD and the software supply chain with keyless auth, ephemeral environments, and trusted tooling.
• Build and operate locked-down partner environments with private networking, default-deny egress, just-in-time access, and audit logging.
• Keep GPU-heavy training and inference reliable and cost-efficient, with observability, SLOs, and on-call readiness.

Skills

Kubernetes operations

Python scripting

Shell scripting

GitOps workflows

Tools

GCP

AWS

Job description

Schrödinger is hiring a Senior Machine Learning Infrastructure Engineer in New York to build and maintain the systems that run ML models and AI agents for drug discovery. You will own the infrastructure across our clusters and customer clouds on GCP and AWS, focusing on repeatable deployments, strong isolation, and cost-efficient GPU workloads.

You will work closely with ML engineers and scientists to design secure, scalable environments, implement GitOps-driven workflows, and ensure reliable,