Our mission is to detect cancer early, when it can be cured. We are working to change the trajectory of cancer mortality and bring stakeholders together to adopt innovative, safe, and effective technologies that can transform cancer care.
We are a healthcare company, pioneering new technologies to advance early cancer detection. We have built a multi-disciplinary organization of scientists, engineers, and physicians and we are using the power of next-generation sequencing (NGS), population-scale clinical studies, and state-of-the-art computer science and data science to overcome one of medicine’s greatest challenges.
GRAIL is headquartered in the bay area of California, with locations in Washington, D.C., North Carolina, and the United Kingdom. It is supported by leading global investors and pharmaceutical, technology, and healthcare companies.
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.
This is a hybrid role based in either Menlo Park, CA (moving to Sunnyvale, CA in Fall 2026) or Durham, NC. Our current hybrid policy requires on-site presence at least 60% of the time, including key in-person collaboration days.
Responsibilities
Design and implement software systems that turn raw clinical, lab, and operational data into reliable, analysis-ready datasetsPartner with scientists, clinicians, lab operations, and data teams to understand data generation, transformation, and usage needsDevelop services, libraries, data models, and workflow components that enforce data integrity, access control, and compliance by designNavigate complex data requirements such as schema evolution, blinding, consent, and privacy complianceCollaborate on cross-functional initiatives involving data quality, testing strategy, monitoring, and operational excellenceLead software engineering efforts for long-lived systems that must evolve alongside active clinical and research programsMentor engineers and collaborate with scientists to ensure software decisions support both technical and scientific outcomes[Contribute to documentation, onboarding materials, and processes that support cross-functional adoption and data literacy across teams][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
BS in Computer Science, Bioinformatics, or a related field, or equivalent practical experience7+ years of experience building production-grade software systemsStrong software engineering fundamentals, including system design, data modeling, API design, and writing well-tested production code.Experience building and operating data-intensive software systems, not just declarative pipelines or SQL-only workflowsProficiency in Python or Go (or similar general-purpose language)Experience with data modeling, validation, and transforming real-world data into usable formats2+ years experience working in regulated or clinical data environments (e.g., HIPAA, CLIA, GCP, FDA compliance)Direct experience working with or supporting scientific teams (e.g., bioinformatics, wet lab, clinical research)
Preferred Qualifications
Advanced degree (MS or PhD) in computer science, bioinformatics, engineering, or a related disciplineExperience designing systems that manage laboratory or bioinformatics data (e.g., LIMS, sequencing pipelines, assay metadata)Familiarity with GxP practices and regulatory reporting requirements in clinical studies is a plusPrior experience working in biotech, diagnostics, or life sciences companiesExperience supporting sample tracking, structured scientific data pipelines, or cross-functional data lifecycle managementExperience designing systems with data sequestration, permissioning, or privacy controlsExperience writing or contributing to software libraries, shared tooling, or reusable components used by other teams
