Staff Bioinformatics Software Engineer #4151

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 Menlo Park, 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.
For more information, please visit grail.com.
We are seeking a Staff Bioinformatics Software Engineer to join our Research team and help drive the development of production-quality software at the heart of GRAIL’s multi-cancer early detection product. This role is critical in bridging the gap between exploratory research and clinical-grade, regulated software, and ensuring that experimental insights are translated into robust, scalable tools used in real-world cancer detection.You will lead verification strategy, test automation, and infrastructure design for bioinformatics and machine learning pipelines, working closely with cross-functional teams to ensure software quality, reproducibility, and regulatory compliance. The ideal candidate brings strong software engineering fundamentals, experience with scientific testing and SDLC processes, and thrives in collaborative environments.In addition to supporting production workflows, you’ll also contribute to early-stage development across diverse domains including statistical modeling, machine learning infrastructure, and genomic data analysis, helping shape the next generation of GRAIL’s technology.
This role is hybrid, 2 days in office, 3 from home in Menlo Park, CA

Responsibilities

• Translate research code and exploratory analysis into robust, scalable software modules for regulated medical devices at the core of GRAIL’s cancer detection products.
• Lead the design and implementation of verification strategies and automated tests for ML components, data pipelines, and bioinformatics software.
• Build and maintain automated testing infrastructure to support continuous verification of production and device software, ensuring test coverage, reproducibility, and compliance.
• Drive improvements in CI/CD pipelines for bioinformatics and ML workflows, ensuring fast, secure, and reproducible deployment of production and experimental code.
• Identify and implement high-performance, scalable solutions to improve the robustness, efficiency, and security of clinical and research bioinformatics pipelines.Follow GRAIL’s SDLC processes to ensure release quality, traceability, and compliance.
• Prototype novel tools and analytical methods to support quality assurance and interpretability of cancer signals across multiple data types.
• Engage with cross-functional teams (ML, bioinformatics, clinical, regulatory) to lead ambiguous or emerging projects, bringing clarity and direction through engineering leadership.

Preferred Qualifications

• Advanced degree (Master’s or PhD) in Computer Science, Bioinformatics, Computational Biology, or a related quantitative discipline.
• 5+ years of industry experience developing production-quality bioinformatics software, pipelines, or infrastructure.
• Demonstrated expertise in verification planning and automated testing for scientific or regulated software.
• Fluency in Python, with strong proficiency in at least one additional language such as Go, C++, or Java.
• Practical experience analyzing next-generation sequencing (NGS) data, with a focus on DNA methylation, epigenomics, or related genomics applications.
• Familiarity with bioinformatics file formats and toolchains (e.g., FASTQ, BAM, VCF; samtools, bcftools, Picard).
• Hands-on experience with cloud computing platforms (AWS, GCP, Azure) and familiarity with containerization (Docker/Kubernetes).
• Solid understanding of regulated software development lifecycles.
• Proven ability to independently lead projects, collaborate cross-functionally, and navigate ambiguity in a dynamic research or product development setting.
• ​Highly Preferred:
• Familiarity with workflow or pipeline frameworks, such as Reflow, Nextflow, Airflow, or similar.
• Experience with machine learning infrastructure, including model training orchestration, experiment tracking, and deployment workflows.
• Applied experience with statistical modeling, machine learning, or development of interpretability tools for biological data.
• Knowledge of reproducibility tooling (e.g., containerization, workflow versioning, dependency locking).

Expected full time annual base pay scale for this position is $163K-$204K. Actual base pay will consider skills, experience and location.
Based on the role, colleagues may be eligible to participate in an annual bonus plan tied to company and individual performance, or an incentive plan. We also offer a long-term incentive plan to align company and colleague success over time.
In addition, GRAIL offers a progressive benefit package, including flexible time-off, a 401k with a company match, and alongside our medical, dental, vision plans, carefully selected mindfulness offerings.
GRAIL is an Equal Employment Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other legally protected status. We will reasonably accommodate all individuals with disabilities so that they can participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. GRAIL maintains a drug-free workplace.