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On-siteFull-timeMid levelEngineeringNo visa sponsorship
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$200K–$335K/yr
Estimate — the employer hasn't published pay.
Based on 32 similar mid-level Engineering (General) jobs in San Francisco that show pay. The range is the middle half of their posted pay (25th to 75th percentile), not an offer — ask the employer for the real range.
Engineering (General) salaries →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.
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.
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.
Visa sponsorship is not available.
On-site in San Francisco, California, United States.