We’re seeking a Senior Machine Learning Infrastructure Engineer to join us in our mission to transform the discovery of therapeutics and materials.

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