team building AI and machine learning models for biomedical applications

Join a team building AI and machine learning models for biomedical applications. As a hands-on platform engineer, you will develop and operate the data infrastructure that transforms clinical, genomic, imaging, and other biomedical data into secure, reliable assets for research, model development, and deployment., * Build and maintain cloud data platforms, including object storage, lakehouse layouts, catalogs, lineage, dataset versioning, and query and access layers.

• Design and operate systems for clinical and molecular data, including harmonization, metadata, dataset releases, retention, governance, and data integration.
• Develop reproducible bioinformatics workflows for omics data, using containerized pipelines and cloud batch compute.
• Build data quality checks, biological validation, and visualizations that help teams identify and resolve data issues.
• Deliver training-ready datasets and versioned data releases, plus interfaces for deploying data alongside models in partner environments.
• Operate reliable, secure, observable, and cost-effective production infrastructure, including Kubernetes clusters and distributed workload orchestration.

• Two to five years of experience building data infrastructure and production pipelines in biotech, diagnostics, biobanking, health data, or a related field working with sensitive biological or clinical data.
• Production experience operating Kubernetes, building infrastructure as code with Terraform or similar tools, and designing CI/CD and deployment systems.
• Strong Python and/or Go skills, with experience owning infrastructure reliability, observability, security, and cost optimization.
• Experience with distributed job scheduling and orchestration, large-scale data pipelines, and infrastructure supporting machine learning workloads; GPU clusters or distributed training experience is valuable.
• Familiarity with a major cloud provider, preferably AWS, as well as workflow tools such as Nextflow, lakehouse and warehouse design, data catalogs, and Docker.
• Experience with omics or bioinformatics workflows and biomedical data standards such as OMOP or FHIR is useful. R or SQL experience is a plus.
• Familiarity with compliance controls such as HIPAA, GDPR, or SOC 2 and associated audit evidence is beneficial.

Base salary: $180,000 to $240,000 annually. Visa sponsorship is not available.