Platform Engineer, Biotech

Clera
San Francisco, CA
On-siteCareer-pivot friendly

Who this role is best for

Strong fit for data engineers with biotech experience who build cloud infrastructure and optimize distributed systems.

Best fit for

  • Data engineers with biotech or health data experience who build cloud infrastructure and optimize distributed systems
    — “building and operating data infrastructure that turns clinical, genomic, imaging, and other biomedical data into reliable assets”
  • Candidates with production pipeline experience in Kubernetes and Terraform, and a track record of deploying Python/Go code
    — “Production experience operating Kubernetes, writing infrastructure as code with Terraform or similar tools”
  • Individuals who have designed CI/CD systems and operated machine learning infrastructure with GPU or distributed training
    — “Experience designing CI/CD and deployment systems, job orchestration, and infrastructure for machine learning workloads”

Things to consider

  • Must be based in or willing to relocate to San Francisco for a hybrid role
    — “Hybrid role based in San Francisco, California. Candidates must be based in or willing to relocate to San Francisco.”
  • No visa sponsorship available, which may limit candidate pool to US citizens
    — “Visa sponsorship is not available.”

How to stand out

  • Highlight experience with governed data stores and compliance controls like HIPAA or SOC 2
    — “Understanding of compliance controls such as HIPAA, GDPR, or SOC 2”
  • Emphasize hands-on work with bioinformatics pipelines and omics data such as WGS or proteomics
    — “Develop reproducible bioinformatics pipelines for omics data, using containerized workflows and cloud batch compute”
  • Showcase infrastructure as code and cloud platform expertise, preferably with AWS
    — “Familiarity with a major cloud provider, preferably AWS”
  • Demonstrate experience with data lineage, versioning, and catalog systems
    — “Build and maintain cloud data platform components, including object storage, lakehouse architecture, catalogs, lineage, versioning”
  • Demonstrate ability to implement data quality checks and biological validation
    — “Implement data quality checks, biological validation, and visualizations that help teams identify issues”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • build data infrastructure
  • operate production systems
  • support machine learning
Typical background
data engineercloud engineer

Skills & requirements

Required

Cloud Data PlatformData Quality ChecksData DeploymentKubernetes

Preferred

BioinformaticsOmics DataHipaa Compliance

Stack & domain

PythonGoTerraformKubernetesAWSNextflowOMOPFHIRRSQLProblem-solvingCommunicationTeamworkBiotechAIData Infrastructure

About the role

Original posting from Clera via Ashby

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.

Source: Clera careers (Ashby)

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