Senior ML Infra Engineer: Kubernetes, GPUs & GitOps

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

Benefits offered by this job

Healthcare (dental & vision)

401k

Flexible work schedule

Parental leave

Paid vacation

Company events

Equity-based compensation

Job summary

Schrödinger is seeking a Senior Machine Learning Infrastructure Engineer in New York to own end-to-end ML infrastructure across our clusters and customer clouds (GCP/AWS). You will drive repeatable customer deployments, secure environments, and cost-efficient GPU systems while enabling AI agents and ML workflows on Kubernetes.

You will collaborate with ML engineers and scientists, ensuring isolation, reliability, and observability.

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 runners, and pinned, signed images.
• 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, backed by observability, SLOs, and on-call.

Skills

Terraform/OpenTofu writer

Python

Shell scripting

Security-minded engineer

Clear communicator

Tools

Kubernetes

GCP

AWS

Argo CD

OpenTofu

Helm

Job description

Schrödinger is seeking a Senior Machine Learning Infrastructure Engineer in New York to own end-to-end ML infrastructure across our clusters and customer clouds (GCP/AWS). You will drive repeatable customer deployments, secure environments, and cost-efficient GPU systems while enabling AI agents and ML workflows on Kubernetes.

You will collaborate with ML engineers and scientists, ensuring isolation, reliability, and observability.