5+ years in platform, infrastructure, or site reliability engineering in a high-growth startup or demanding environment.
Deep expertise in AWS or GCP, including production Kubernetes and infrastructure-as-code tools like Terraform.
Proficient in Python and/or Go with experience in CI/CD pipeline design and deployment systems.
Experience with job orchestration or scheduling systems for distributed workloads; GPU or machine learning infrastructure is a plus.
Familiarity with biological, clinical, or sensitive data and production data pipelines, including Nextflow or similar workflow managers.
Responsibilities
Build and operate cloud data infrastructure, including object storage and lakehouse architecture.
Design and maintain governed data warehouses for clinical records and molecular features.
Develop reproducible omics pipelines for data types like whole-genome and RNA sequencing.
Implement data quality checks and visualizations to identify processing issues.
Prepare training-ready dataset releases and build deployment interfaces for models in partner environments.
Operate production cloud infrastructure, Kubernetes clusters, and CI/CD systems with a focus on reliability and security.
Benefits
On-site work in San Francisco, California.
Opportunity to work with a multidisciplinary AI and life sciences team.
Engagement in cutting-edge cloud data platform development.
Full Job Description
About the Role
Join a multidisciplinary AI and life sciences team building the cloud data platform that turns clinical, genomic, imaging, and other biomedical data into reliable, model-ready assets. This hands-on role combines platform engineering, data infrastructure, and bioinformatics workflows to support internal teams and external partners. What You'll Do
Build and operate cloud data infrastructure, including object storage, lakehouse architecture, catalogs, lineage, versioning, and access layers.
Design and maintain governed data warehouses for clinical records, molecular features, metadata, and versioned dataset releases.
Develop reproducible omics pipelines for data such as whole-genome sequencing, RNA sequencing, and proteomics using containerized workflows and cloud batch compute.
Implement data quality checks, biological validation, and visualizations that help teams identify issues across processing steps.
Prepare training-ready dataset releases and build data deployment interfaces for models used in partner environments.
Build and operate production cloud infrastructure, Kubernetes clusters, CI/CD systems, and distributed workload orchestration, with attention to reliability, security, observability, and cost.
What We're Looking For
5+ years of experience in platform, infrastructure, or site reliability engineering, including operating cloud infrastructure in a high-growth startup or technically demanding environment.
Deep AWS or GCP experience, including production Kubernetes, and experience with Terraform or similar infrastructure-as-code tools.
Production programming experience in Python and/or Go, plus experience designing CI/CD pipelines and deployment systems.
Experience building job orchestration or scheduling systems for distributed workloads; GPU or machine learning infrastructure experience is a plus.
Familiarity with biological, clinical, or other sensitive data and production data pipelines. Experience with Nextflow or similar workflow managers, lakehouse and warehouse design, data catalogs, Docker, and cloud batch compute is relevant.
Python, R, SQL, and familiarity with standards such as OMOP or FHIR are useful.
Compensation & Benefits
Visa sponsorship is not available. Location
On-site in San Francisco, California, United States.
