Clera · San Francisco
Skills: Python, SQL, AWS, Docker, CI/CD
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.…
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