Platform Engineer II - Observability
Build and maintain scalable observability platforms, telemetry pipelines, and shared frameworks that help engineering teams understand service health, diagnose issues, and improve reliability.
Join a biotech AI team building data infrastructure that turns clinical, genomic, imaging, and other biomedical data into reliable, model-ready assets. You will help design and operate the platform, with a focus on secure, governed data systems and production infrastructure for scientific and machine learning teams.
Build and maintain cloud data platform components, including object storage, lakehouse architecture, catalogs, lineage, dataset versioning, and query and access layers.
Design and operate data warehouses and governed systems for clinical records, molecular features, metadata, retention, and dataset releases.
Develop reproducible bioinformatics workflows for omics data, using containerized pipelines and cloud batch compute.
Implement data quality checks, biological validation, and visualizations that help teams identify issues across processing steps.
Deliver versioned, training-ready datasets and data deployment interfaces for model use in partner environments.
Operate production infrastructure, including Kubernetes, CI/CD, workload orchestration, security, reliability, observability, and cost optimization.
Two to five years of experience building data infrastructure and production pipelines as a platform engineer in biotech, diagnostics, biobanking, health data, or a related field working with sensitive biological or clinical data.
Production experience operating Kubernetes clusters, writing infrastructure as code with Terraform or similar tools, and shipping tested, documented Python and/or Go code.
Experience designing CI/CD and deployment systems, orchestrating distributed workloads, and owning infrastructure reliability, observability, security, and cost.
Familiarity with a major cloud provider, preferably AWS, plus Docker, workflow managers such as Nextflow, lakehouse and warehouse design, data catalogs, and orchestration.
Experience with large-scale data pipelines, machine learning infrastructure, or GPU workloads is valuable. Knowledge of omics workflows, bioinformatics tools, OMOP, FHIR, R, or SQL is also relevant.
Experience implementing compliance controls and audit evidence for standards such as HIPAA, GDPR, or SOC 2 is beneficial.
Base salary: $180,000 to $240,000 annually. Visa sponsorship is not available.
Hybrid role based in San Francisco, California. Candidates must be local or willing to relocate.
Build and maintain scalable observability platforms, telemetry pipelines, and shared frameworks that help engineering teams understand service health, diagnose issues, and improve reliability.
Build AI agents and platform automation across Kubernetes, GitOps, CI/CD, and developer self-service to reduce operational toil and help engineering teams deliver and resolve issues faster.
Design and maintain secure, scalable cloud infrastructure, improve infrastructure-as-code and CI/CD workflows, and build automation and observability that strengthen reliability and developer efficiency.
Install, operate, and secure OpenShift and Kubernetes platforms, supporting scalable containerized deployments while partnering with development, operations, and security teams on reliability, automation, and platform best practices.
Build and improve Azure-based platform foundations for AI workloads, operating Kubernetes and Databricks services while enabling teams to deploy, monitor, govern, and scale systems reliably.