Sr Director Software Engineer

Accountabilities:

• • Define and own the software engineering vision, technical roadmap, delivery priorities, operating model, resource plans, and engineering standards for a bioinformatics software engineering organization.
  • Lead, mentor, hire, and develop software engineers, bioinformatics engineers, technical leads, and managers while fostering accountability, collaboration, technical excellence, and continuous improvement.
  • Oversee the architecture, development, implementation, maintenance, and operational support of production-grade software platforms, bioinformatics workflows, data systems, and reporting solutions.
  • Drive modernization initiatives involving legacy systems, workflow orchestration, automation, containerization, cloud and HPC computing, data integration, APIs, and scalable software architectures.
  • Establish strong production operations practices, including monitoring, observability, incident response, escalation processes, root-cause analysis, post-release support, and reliability improvements.
  • Partner with R&D, clinical laboratory, quality, operations, product, data, and executive stakeholders to translate scientific, clinical, and business priorities into actionable software roadmaps.
  • Lead prioritization, scope definition, dependency management, resource planning, delivery governance, and executive communication across complex software initiatives.
  • Guide research algorithms, analytical prototypes, scripts, and proof-of-concept tools through the transition into scalable, tested, validated, documented, and maintainable production workflows.
  • Establish and enforce software development lifecycle standards covering requirements, architecture reviews, code reviews, automated testing, CI/CD, validation, documentation, release management, and change control.
  • Ensure software practices meet applicable quality, regulatory, privacy, security, traceability, and audit-readiness expectations within clinical diagnostics and other regulated environments.
  • Define and monitor engineering KPIs covering roadmap delivery, system reliability, software quality, technical debt, production incidents, team health, stakeholder satisfaction, and operational efficiency.
  • Lead responsible adoption of AI-enabled engineering, workflow automation, documentation, decision-support, code assistance, issue triage, and operational capabilities, with appropriate governance and human oversight.
  • Reduce technical debt and improve scalability, maintainability, performance, deployment efficiency, automation, documentation, and integration across critical platforms and workflows.
  • Evaluate technology investments, build-versus-buy decisions, architecture patterns, and emerging technologies to ensure long-term alignment with organizational strategy.
  • Communicate technical decisions, risks, tradeoffs, roadmap progress, and recommendations effectively to executive, technical, scientific, clinical, quality, and business audiences.

  Requirements:

  • Bachelor’s degree in Computer Science, Software Engineering, Bioinformatics, Computational Biology, Genomics, Data Science, or another relevant technical or quantitative field.
  • 12+ years of experience in software engineering, bioinformatics software development, clinical genomics technology, data platforms, or related computational technology disciplines.
  • 7+ years of software engineering leadership experience, including people management, hiring, performance management, mentoring, organizational planning, and leadership of technical leads or managers.
  • Demonstrated success defining engineering strategy, technical roadmaps, delivery priorities, resource plans, and operating models for complex software organizations or platforms.
  • Proven experience leading production-grade software systems, bioinformatics workflows, data platforms, workflow automation, or reporting applications in clinical, healthcare, laboratory, or regulated environments.
  • Strong expertise in software architecture, system design, APIs, data integration, database-backed applications, workflow orchestration, scalable computing, and modern software development practices.
  • Experience establishing or improving engineering standards covering requirements management, code review, automated testing, CI/CD, validation, documentation, release management, incident management, and production support.
  • Practical understanding of NGS data, clinical genomics workflows, bioinformatics pipelines, genomic data formats, variant analysis, laboratory interfaces, or diagnostic reporting systems.
  • Experience transforming research prototypes, analytical methods, scripts, or proof-of-concept solutions into scalable, validated, documented, and maintainable production workflows.
  • Experience operating within quality, regulatory, privacy, security, traceability, and audit-readiness frameworks relevant to clinical diagnostics, healthcare technology, laboratory operations, or regulated software.
  • Strong technical fluency with Git-based development, automated testing, CI/CD, Linux, containers, workflow orchestration, cloud or HPC computing, databases, APIs, and monitoring or observability tools.
  • Experience with technologies such as Nextflow, Docker, Kubernetes, cloud platforms, microservices, API-driven architectures, data lakes, data warehouses, and enterprise data integration is highly valuable.
  • Familiarity with AI-enabled engineering tools, large language models, agentic AI, automation, decision-support systems, or AI-assisted operational workflows is preferred.
  • Master’s degree or PhD in a relevant technical field is preferred.
  • Experience with CAP/CLIA-regulated laboratory environments, molecular diagnostics, clinical genomics, variant interpretation, healthcare interoperability, or related domains is a strong advantage.
  • Excellent written and verbal communication skills, with the ability to translate complex technical and scientific concepts for diverse stakeholders.
  • Strong judgment, analytical thinking, problem-solving ability, and a practical approach to balancing quality, delivery, scalability, and business priorities.
  • Demonstrated ability to collaborate effectively with scientists, engineers, clinical teams, laboratory personnel, quality professionals, and senior business leaders.
  • Commitment to reproducibility, documentation, automation, maintainability, operational excellence, and high-quality engineering practices.

  Benefits:

  • Salary: Competitive compensation package based on experience, qualifications, and role requirements.
  • Work arrangement: Fully remote position based in the United States.
  • Opportunity to lead technology strategy for software platforms supporting clinical genomic diagnostics and scientific innovation.
  • Senior-level ownership of engineering architecture, organizational development, modernization, and delivery strategy.
  • Opportunity to work cross-functionally with software engineering, bioinformatics, R&D, clinical laboratory, quality, operations, product, and executive teams.
  • Professional growth through leadership of experienced engineering teams and exposure to advanced genomics, regulated technology, cloud/HPC platforms, and AI-enabled engineering.
  • Inclusive workplace committed to equal employment opportunity and a diverse workforce.
  • Reasonable accommodations are available during the application process for qualified candidates.
  • Remote work environment with responsibilities that may occasionally involve exposure to bloodborne or airborne pathogens or infectious materials.