Mid-Level Platform Engineer, Biotech

About the Role

Join the platform and data engineering team at an AI-focused biotech startup, building and operating infrastructure for sensitive clinical and biomedical data. Your work will help scientists and machine learning engineers access reliable, model-ready datasets and deploy data and models into partner environments.

What You'll Do

• Build and maintain cloud data infrastructure, including Kubernetes clusters, object storage, lakehouse layouts, catalogs, lineage, versioning, and data access systems.

• Design and operate data warehouses and lifecycle processes for clinical records, molecular features, metadata, and versioned dataset releases.

• Develop reproducible bioinformatics workflows for omics data, using workflow managers such as Nextflow and containerized cloud compute.

• Build data quality checks, biological validation, and visualizations that help teams identify issues throughout processing.

• Create reliable, versioned interfaces for preparing model-training datasets and delivering data alongside models to partner environments.

• Own infrastructure reliability, observability, security, compliance controls, and cost optimization, including CI/CD and workload orchestration.

What We're Looking For

• Two to five years building data infrastructure and production pipelines, with experience in biotech, diagnostics, biobanking, or health data involving sensitive or biological information.

• Production experience building and operating Kubernetes clusters, writing infrastructure as code with Terraform or similar tools, and designing CI/CD and deployment systems.

• Strong production programming skills in Python and/or Go, with clean, tested, well-documented code and effective code review practices.

• Experience with a major cloud provider, preferably AWS, distributed job orchestration, and large-scale data pipelines.

• Familiarity with lakehouse and warehouse design, data catalogs, Docker, and workflow managers such as Nextflow.

• Experience with GPU or machine learning infrastructure, distributed training, omics workflows, or clinical data standards such as OMOP and FHIR is valuable.

• Knowledge of controls and audit practices for HIPAA, GDPR, or SOC 2 is beneficial; R or SQL experience is also useful.

Compensation & Benefits

Salary range: $180,000 to $240,000 USD annually. Visa sponsorship is not available.

Location

Hybrid role based in San Francisco, California.