- Schrdinger
- New York, NY
- <48 Hours
- $110,000–$160,000
Senior Machine Learning Infrastructure Engineer.
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Senior Machine Learning Infrastructure Engineer: our view in 3 lines...
- The Role:This role is for an engineer who builds and runs the infrastructure that deploys machine learning models and AI agents for drug discovery software.
- The Person:The person will own infrastructure for customer deployments, design isolation for multi-tenant agent sandboxes, secure CI/CD and the software supply chain, and keep GPU training and inference reliable and cost-efficient.
- Requirements:The ideal candidate has at least six years of infrastructure, platform, or SRE experience, in-depth understanding of Kubernetes operation, Terraform or OpenTofu and Helm, Python and shell scripting, and robust GCP background.
About the role
We’re seeking a Senior Machine Learning Infrastructure Engineer to join us in our mission to transform the discovery of therapeutics and materials.
As a member of the Machine Learning team, you’ll play a key role in building and maintaining the systems that put ML models and AI agents to work in drug discovery. LiveDesign ML trains and serves models on live project data, both in our own clusters and in customers' LiveDesign environments. Bunsen is an agentic platform where every session runs an AI agent in its own isolated Kubernetes pod. We also run locked-down environments for federated-learning collaborations with pharma partners.
Who will love this job:
- A Terraform or OpenTofu and Helm writer who has shipped with GitOps
- A solid Python and shell scripter
- A security-minded engineer who defaults to least privilege, managed secrets, and segmented networks
- A clear communicator who enjoys working closely with ML engineers and scientists
What you’ll do:
- 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
What you should have:
- 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)
We’d prefer to hire someone who has experience with:
- Running GPU workloads and model serving (vLLM, Knative) on Kubernetes
- Sandboxing untrusted code (gVisor, Kata Containers, Cilium)
- Shipping software into customer-owned or restricted-egress clouds
- Argo Workflows or batch/HPC scheduling
- Regulated environments (SOC 2, ISO 27001, GxP)
- Operating LLM or agent platforms

