About the position
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.
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.
Requirements
- 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.
Nice-to-haves
- 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).
Benefits
- Flexible time-off
- 401k with a company match
- Medical, dental, vision plans
- Mindfulness offerings
- Annual bonus plan tied to company and individual performance
- Long-term incentive plan to align company and colleague success over time
