Senior Software Engineer, Genomic Platform

About the Role

We are on a mission to make genomic insights a routine part of clinical care. We are looking for a Senior Software Engineer to architect and build the core backend platform that transforms massive-scale genomic and scientific data into clear, actionable insights for clinicians and patients.

In this role, you won't just be writing code; you'll be solving complex distributed systems problems at the intersection of biology, data science, and clinical medicine. You will design and orchestrate the analytical pipelines that process petabytes of data, ensuring they are robust, reproducible, and compliant with the stringent requirements of a regulated clinical environment. Your work will directly enable our scientists and bioinformaticians to deliver life-changing diagnostics at scale.

What You'll Do

• Architect and own the core backend services for our genomic diagnostics platform, from raw data ingestion to the delivery of clinical reports.
• Design, build, and optimize production-grade analytical pipelines for processing large-scale genomic datasets, with a focus on reliability, scalability, and reproducibility.
• Implement and manage complex workflow orchestration using tools like Nextflow, Cromwell, or Airflow to coordinate distributed, multi-step analyses.
• Build and maintain scalable, cost-effective cloud infrastructure on Google Cloud Platform (GCP) to handle high-volume, sequential data processing (e.g., GCS, GKE, Cloud Life Sciences API).
• Ensure rigorous data privacy and security by implementing best practices for handling PHI and maintaining compliance with clinical and regulatory standards (e.g., HIPAA, CLIA/CAP).
• Champion software engineering best practices including CI/CD, containerization (Docker), comprehensive testing, and infrastructure-as-code to ensure platform stability and velocity.
• Collaborate deeply with a cross-functional team of bioinformaticians, clinical scientists, and product managers to translate complex scientific questions into robust, scalable engineering solutions.

Who You Are

• You are a builder with significant experience in backend development within a data-intensive environment. You've built and maintained systems that handle large, complex datasets.
• You have a strong foundation in scientific computing. Whether your background is in computer science, computational biology, or a related field, you are comfortable with the paradigms of scientific data analysis (e.g., matrix operations, statistical methods, file-based workflows).
• You are an expert in workflow orchestration. You understand the challenges of managing complex task dependencies and have hands-on experience with tools like Nextflow, WDL/Cromwell, or Airflow in a production setting.
• You speak "cloud" fluently. You have deep experience with cloud platforms, ideally GCP, and know how to design systems for scalability, resilience, and cost-efficiency.
• You are a champion of reproducibility. You understand that containerization (Docker) is non-negotiable for ensuring that pipelines run identically in development, staging, and production.
• You have a high bar for quality and compliance. You are meticulous about data privacy (PHI), validation, and security, especially when working with clinical data.
• You are a collaborative partner. You can hold your own in a technical discussion with a bioinformatician and can clearly explain complex architectural decisions to non-technical stakeholders.

Must-Haves

• Experience: 5+ years of software engineering experience, with at least 3 years focused on backend systems for large-scale scientific or genomic data.
• Technical Depth:
• Expert-level proficiency in Python or Java.
• Proven experience designing and deploying analytical pipelines with workflow orchestration tools (Nextflow, Cromwell, or Airflow).
• Deep expertise with Docker for containerization in production environments.
• Strong experience with a major cloud provider (GCP preferred), including compute, storage, and networking services.
• Domain Knowledge: Proven ability to work with large-scale genomic or scientific datasets (e.g., whole-genome sequencing, proteomics, high-throughput screening data).
• Engineering Excellence: Strong understanding of software development best practices, including version control (Git), testing, and CI/CD.

Nice-to-Haves

• Experience working in a regulated environment (HIPAA, CLIA, GxP, or similar).
• Familiarity with high-performance computing (HPC) workload managers (e.g., SLURM, PBS) and hybrid cloud/HPC architectures.
• Experience with data warehousing and querying tools like BigQuery, Hail, or Apache Spark.
• Contributions to open-source projects in the bioinformatics or scientific computing space.