Sr. Platform Engineer, ML Infrastructure

Insilico Search Partners; Cambridge (MA); On-site; USD 140,000 - 210,000; Full time

Job summary

Insilico Search Partners is seeking an experienced infrastructure/ML Ops engineer to own production-grade Kubernetes clusters and data pipelines. The role focuses on scaling ML workloads, implementing CI/CD and GitOps, and ensuring reliable, observable infrastructure across on-prem and cloud.

You will collaborate with ML engineers and researchers, manage GPU workloads, and drive cross-functional platform initiatives to reduce toil and improve repeatability.

Qualifications

• 5+ years in production infrastructure, platform engineering, DevOps, SRE, or MLOps.
• Hands-on Kubernetes and infrastructure-as-code experience.
• Experience with major cloud platforms, CI/CD pipelines and containerization.
• Familiarity with ML workflows and model-serving platforms.

Responsibilities

• Manage infrastructure as code (Terraform) and production Kubernetes clusters.
• Build and maintain CI/CD and GitOps workflows; secure and optimize container environments.
• Operate workflow orchestration and storage for large ML datasets.
• Manage GPU-enabled infrastructure: provisioning, drivers, utilization.
• Establish observability (metrics, logs, dashboards, alerting) and runbooks.
• Deploy and operate ML training/inference infrastructure and tooling for experiment tracking.
• Partner with ML Engineers to move model workloads onto production-ready infrastructure.

Skills

Kubernetes

Terraform

CI/CD

GitOps

Workflow orchestration

Cloud platforms

Tools

Kubeflow

Argo CD

Nextflow

Ray

Vertex AI

Argo Workflows

Helm

CircleCI

GitHub Actions

Seqera

Job description

About the Company

Our client is a venture-backed biotech company applying AI to drug discovery, using a proprietary platform to identify novel drug targets and therapeutics from complex biological data.

Own and evolve the shared infrastructure — Kubernetes, Terraform, CI/CD, orchestration, storage, security, observability, and GPU systems — behind the company's ML and scientific workloads, across on-prem and cloud. You'll work closely with Machine Learning Engineers and researchers to keep training, evaluation, and inference reliable and scalable. Infrastructure-first role, with meaningful ML systems ownership (~60/40 split).

Key Responsibilities

• Manage infrastructure as code (Terraform) and operate production Kubernetes clusters — networking, IAM/RBAC, security, resource allocation, reliability.


• Build and maintain CI/CD and GitOps workflows (GitHub Actions, CircleCI, Helm, Argo CD); secure and optimize container environments.


• Operate workflow orchestration (Kubeflow, Argo Workflows, Nextflow, or Seqera) and storage infrastructure for large scientific/ML datasets.


• Manage GPU-enabled infrastructure: provisioning, scheduling, drivers, utilization, troubleshooting.


• Establish observability (metrics, logs, dashboards, alerting) and document architecture/runbooks so operations don't depend on one person.


• Deploy and operate ML training/inference infrastructure (Anyscale, Ray, Vertex AI, Kubernetes); own shared tooling for experiment tracking and model/data versioning.


• Partner with ML Engineers to move model workloads onto shared, production-ready infrastructure.


• Lead cross-functional infrastructure initiatives and mentor engineers/scientists on the platform.

Qualifications

• 5+ years in production infrastructure, platform engineering, DevOps, SRE, or MLOps.


• Strong hands-on Kubernetes and infrastructure-as-code (Terraform) experience.


• Experience with major cloud platforms, CI/CD, containerization, and GPU/compute-intensive workloads.


• Workflow orchestration and GitOps tooling (Kubeflow, Argo, Nextflow, Helm, Kueue).


• Familiarity with PyTorch and distributed ML execution; experience with model-serving platforms (Ray, Vertex AI).