2-5 years of experience in data infrastructure and production pipelines in biotech or related fields.
Proficient in operating Kubernetes clusters and writing infrastructure as code with Terraform or similar tools.
Skilled in CI/CD design and deployment systems, with a focus on reliability and observability.
Familiar with major cloud providers, preferably AWS, and tools like Docker and Nextflow.
Experience with large-scale data pipelines and machine learning infrastructure is a plus.
Knowledge of compliance standards such as HIPAA, GDPR, or SOC 2 is beneficial.
Responsibilities
Build and maintain cloud data platform components like object storage and lakehouse architecture.
Design and operate data warehouses for clinical records and molecular features.
Develop reproducible bioinformatics workflows using containerized pipelines.
Implement data quality checks and visualizations to identify processing issues.
Deliver versioned datasets and deployment interfaces for model use.
Operate production infrastructure, focusing on security, reliability, and cost optimization.
Benefits
Hybrid work model based in San Francisco, California.
Opportunity to work with cutting-edge biotech AI technology.
Engagement in impactful projects that contribute to biomedical advancements.
Collaborative team environment with a focus on innovation.
Full Job Description
About the Role
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. What You'll Do
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.
What We're Looking For
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.
Compensation & Benefits
Base salary: $180,000 to $240,000 annually. Visa sponsorship is not available. Location
Hybrid role based in San Francisco, California. Candidates must be local or willing to relocate.
