Job description

ABOUT THE ROLE Join a team building cloud infrastructure for biomedical data and machine learning. You will develop and operate secure, reliable platforms and pipelines that make clinical and biological data usable for research and model deployment. WHAT YOU'LL DO - Build and maintain cloud data platforms, including storage, lakehouse architecture, catalogs, lineage, dataset versioning, and access layers. - Design and operate Kubernetes clusters, infrastructure-as-code systems, CI/CD pipelines, and deployment systems for production workloads. - Create and maintain reproducible bioinformatics pipelines for genomics, proteomics, and other biomedical data using workflow tools and cloud batch compute. - Build data quality checks, biological validation, and visualizations that help teams identify issues in datasets and pipeline outputs. - Develop orchestration and scheduling for distributed workloads, and prepare versioned datasets for model training and partner deployment. - Own infrastructure reliability, observability, security, and cost optimization, including support for GPU and distributed machine learning workloads. WHAT WE'RE LOOKING FOR - Two to five years of experience building data infrastructure and production pipelines, ideally in biotech, diagnostics, biobanking, health data, or a related field. - Production experience operating Kubernetes clusters and building infrastructure as code with Terraform or similar tools. - Strong Python and/or Go programming skills, with experience shipping tested, documented production code. - Experience designing CI/CD and deployment systems, and building orchestration or scheduling for distributed workloads. - Familiarity with AWS or another major cloud provider, Docker, Nextflow or similar workflow managers, lakehouses, data warehouses, and data catalogs. - Experience with sensitive clinical or biological data and standards such as OMOP or FHIR is valuable; R and SQL are also useful. - Experience with GPU infrastructure, distributed training, or large-scale data pipelines is a plus. - Based in San Francisco or willing to relocate. COMPENSATION & BENEFITS Base salary: $180,000 to $240,000 annually. Visa sponsorship is not available. LOCATION Hybrid in San Francisco, California, United States.

Requirements

Must-haves

Build and maintain cloud data platforms, including storage, lakehouse architecture, catalogs, lineage, dataset versioning, and access layers.
Design and operate Kubernetes clusters, infrastructure-as-code systems, CI/CD pipelines, and deployment systems for production workloads.
Create and maintain reproducible bioinformatics pipelines for genomics, proteomics, and other biomedical data using workflow tools and cloud batch compute.
Build data quality checks, biological validation, and visualizations that help teams identify issues in datasets and pipeline outputs.
Develop orchestration and scheduling for distributed workloads, and prepare versioned datasets for model training and partner deployment.
Own infrastructure reliability, observability, security, and cost optimization, including support for GPU and distributed machine learning workloads.
Two to five years of experience building data infrastructure and production pipelines, ideally in biotech, diagnostics, biobanking, health data, or a related field.
Production experience operating Kubernetes clusters and building infrastructure as code with Terraform or similar tools.
Strong Python and/or Go programming skills, with experience shipping tested, documented production code.
Experience designing CI/CD and deployment systems, and building orchestration or scheduling for distributed workloads.
Familiarity with AWS or another major cloud provider, Docker, Nextflow or similar workflow managers, lakehouses, data warehouses, and data catalogs.
Experience with sensitive clinical or biological data and standards such as OMOP or FHIR is valuable; R and SQL are also useful.
Based in San Francisco or willing to relocate.
Hybrid in San Francisco, California, United States.

Nice-to-haves

Experience with GPU infrastructure, distributed training, or large-scale data pipelines is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningkubernetesci/cdpythongoawsdockersql

Platform: ashby
Effort: quick