2-5 years of experience in biotech, diagnostics, or related fields involving sensitive biological data.
Proficient in Kubernetes and infrastructure as code tools like Terraform.
Skilled in Python and/or Go for production code development.
Experience with CI/CD systems and job orchestration for machine learning workloads.
Familiar with major cloud providers, preferably AWS, and workflow managers like Nextflow.
Knowledge of large-scale data pipelines and bioinformatics data types such as WGS or RNA-seq.
Understanding of compliance standards like HIPAA and strong engineering practices.
Responsibilities
Build and maintain cloud data platform components for data storage and access.
Design and operate governed data stores for clinical and molecular data.
Develop reproducible bioinformatics pipelines using containerized workflows.
Implement data quality checks and visualizations for issue identification.
Prepare training-ready datasets and build deployment interfaces for partners.
Operate production infrastructure, focusing on security and reliability.
Benefits
Hybrid work model based in San Francisco.
Opportunity to work with cutting-edge biotech AI technologies.
Engagement in hands-on data infrastructure projects.
Collaboration with a multidisciplinary team in a growing field.
Full Job Description
About the Role
Join a biotech AI engineering team building and operating data infrastructure that turns clinical, genomic, imaging, and other biomedical data into reliable assets for machine learning. This hands-on role supports data workflows from ingestion and analysis through model training and deployment to partner environments. What You'll Do
Build and maintain cloud data platform components, including object storage, lakehouse architecture, catalogs, lineage, versioning, and access systems.
Design and operate governed data stores and releases for clinical records, molecular features, metadata, and linked datasets.
Develop reproducible bioinformatics pipelines for omics data, using containerized workflows and cloud batch compute.
Implement data quality checks, biological validation, and visualizations that help teams identify issues.
Prepare versioned, training-ready datasets and build data deployment interfaces for partner environments.
Operate production infrastructure, including Kubernetes, CI/CD, distributed workload scheduling, security, observability, reliability, and cost optimization.
What We're Looking For
Two to five years building data infrastructure and production pipelines in biotech, diagnostics, biobanking, health data, or a related setting involving sensitive biological or clinical data.
Production experience operating Kubernetes, writing infrastructure as code with Terraform or similar tools, and shipping tested Python and/or Go code.
Experience designing CI/CD and deployment systems, job orchestration, and infrastructure for machine learning workloads; GPU or distributed training experience is valuable.
Familiarity with a major cloud provider, preferably AWS, plus workflow managers such as Nextflow, lakehouse and warehouse design, data catalogs, and orchestration.
Experience with large-scale data pipelines, bioinformatics or omics data such as WGS, RNA-seq, or proteomics; familiarity with OMOP, FHIR, R, or SQL is helpful.
Understanding of compliance controls such as HIPAA, GDPR, or SOC 2, and strong engineering practices for testing, documentation, code review, and provenance.
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 based in or willing to relocate to San Francisco.
