Join a team building AI and machine learning models for biomedical applications. As a hands-on platform engineer, you will develop and operate the data infrastructure that transforms clinical, genomic, imaging, and other biomedical data into secure, reliable assets for research, model development, and deployment.
Build and maintain cloud data platforms, including object storage, lakehouse layouts, catalogs, lineage, dataset versioning, and query and access layers.
Design and operate systems for clinical and molecular data, including harmonization, metadata, dataset releases, retention, governance, and data integration.
Develop reproducible bioinformatics workflows for omics data, using containerized pipelines and cloud batch compute.
Build data quality checks, biological validation, and visualizations that help teams identify and resolve data issues.
Deliver training-ready datasets and versioned data releases, plus interfaces for deploying data alongside models in partner environments.
Operate reliable, secure, observable, and cost-effective production infrastructure, including Kubernetes clusters and distributed workload orchestration.
Two to five years of experience building data infrastructure and production pipelines in biotech, diagnostics, biobanking, health data, or a related field working with sensitive biological or clinical data.
Production experience operating Kubernetes, building infrastructure as code with Terraform or similar tools, and designing CI/CD and deployment systems.
Strong Python and/or Go skills, with experience owning infrastructure reliability, observability, security, and cost optimization.
Experience with distributed job scheduling and orchestration, large-scale data pipelines, and infrastructure supporting machine learning workloads; GPU clusters or distributed training experience is valuable.
Familiarity with a major cloud provider, preferably AWS, as well as workflow tools such as Nextflow, lakehouse and warehouse design, data catalogs, and Docker.
Experience with omics or bioinformatics workflows and biomedical data standards such as OMOP or FHIR is useful. R or SQL experience is a plus.
Familiarity with compliance controls such as HIPAA, GDPR, or SOC 2 and associated audit evidence 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 based in or willing to relocate to the area.