À propos de ce poste Staff Software Engineer – Data Team #4555 chez Grailbio
GRAIL is seeking a Staff Software Engineer for the Data Team. This team designs, builds, and operates the software systems that manage GRAIL’s end-to-end data lifecycle, from sample ingestion through downstream analysis, while meeting rigorous clinical, regulatory, and privacy standards. Our work directly supports clinical research, operations, and decision-making in the fight against cancer.
In this role, you will take technical ownership of systems that produce trusted, analysis-ready datasets for use across GRAIL’s research and clinical programs. This is a software engineering role focused on building complex production-grade systems that work with data in dynamic, regulated environments as opposed to assembling off-the-shelf ETL tools or writing SQL heavy pipelines,.This position offers significant autonomy and scope for impact. You’ll collaborate closely with research, clinical lab operations, and scientific teams, and lead efforts to improve how we structure, validate, and deliver critical scientific and clinical data.
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This role is based in Menlo Park, California, and will move to Sunnyvale, California in Fall 2026. It offers a flexible work arrangement, with the ability to work from GRAIL's office or from home. Our current flexible work arrangement policy requires that a minimum of 60%, or 24 hours, of your total work week be on-site. Your specific schedule, determined in collaboration with your manager, will align with team and business needs and could exceed the 60% requirement for the site. At our Menlo Park campus, Tuesdays and Thursdays are the key days where we encourage on-site presence to engage in events and on-site activities.
Responsibilities
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Design and implement software systems that turn raw clinical, lab, and operational data into reliable, analysis-ready datasets
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Partner with scientists, clinicians, lab operations, and data teams to understand data generation, transformation, and usage needs
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Develop services, libraries, data models, and workflow components that enforce data integrity, access control, and compliance by design
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Navigate complex data requirements such as schema evolution, blinding, consent, and privacy compliance
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Collaborate on cross-functional initiatives involving data quality, testing strategy, monitoring, and operational excellence
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Lead software engineering efforts for long-lived systems that must evolve alongside active clinical and research programs
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Mentor engineers and collaborate with scientists to ensure software decisions support both technical and scientific outcomes
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[Contribute to documentation, onboarding materials, and processes that support cross-functional adoption and data literacy across teams]
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[Participate in incident response or investigation processes related to data quality or availability issues in production systems]
These responsibilities summarize the role’s primary responsibilities and are not an exhaustive list. They may change at the company’s discretion.
Required Qualifications
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7+ years of experience building production-grade software systems
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Strong software engineering fundamentals, including system design, data modeling, API design, and writing well-tested production code.
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Experience building and operating data-intensive software systems, not just declarative pipelines or SQL-only workflows
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Proficiency in Go or Python (or similar general-purpose language)
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Experience with data modeling, validation, and transforming real-world data into usable formats
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BS in Computer Science, Engineering or Bioinformatics, or a related field, or equivalent practical experience
Preferred Qualifications
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2+ years experience working in regulated or clinical data environments (e.g., HIPAA, CLIA, GCP, FDA compliance)
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Direct experience working with or supporting scientific teams (e.g., bioinformatics, wet lab, clinical research)
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Experience designing systems that manage laboratory or bioinformatics data (e.g., LIMS, sequencing pipelines, assay metadata)
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Familiarity with GxP practices and regulatory reporting requirements in clinical studies is a plus
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Prior experience working in biotech, diagnostics, or life sciences companies
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Experience supporting sample tracking, structured scientific data pipelines, or cross-functional data lifecycle management
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Experience designing systems with data sequestration, permissioning, or privacy controls
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Experience writing or contributing to software libraries, shared tooling, or reusable components used by other teams
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Advanced degree (MS or PhD) in computer science, engineering, bioinformatics or a related discipline
