Senior Software Engineer II

New York Genome Center•New York, NY

About The Position

The Senior Software Engineer II (SSEII) at the New York Genome Center is a senior individual contributor who leads software projects from planning through delivery and production support. The role applies sound judgment across related technical areas and systems, designs durable solutions, mentors engineers, and improves products, integrations, maintenance, and operational processes while anticipating effects on adjacent departments and shared services. The SSEII builds and maintains software supporting genomic testing, including sample accessioning, laboratory and sequencing workflows, computational analysis, clinical reporting, and secure genomic-data exchange. The engineer works directly with scientific, clinical, and operational stakeholders to clarify needs, define requirements, and develop technical designs and maintainable solutions. The role partners across Computational Biology, Clinical Bioinformatics, Sequencing Operations, Project Management, Research Computing, and other groups to deliver reliable services. The position contributes to product requirements and technical direction, improves recurring workflows, applies sound engineering and data-protection practices, and communicates progress, risks, decisions, and delivery expectations. It also mentors engineers and may use approved AI-enabled tools for software development, testing, documentation, and troubleshooting, validating outputs for accuracy, security, privacy, and policy compliance. NYGC is an independent nonprofit academic research organization advancing genomic research, with a focus on disease-based research in neuropsychiatric disease, neurodegenerative disease, and cancer. It is located in Lower Manhattan and works closely with academic medical centers and research universities in the New York region.

Requirements

Bachelor’s degree in Computer Science and 9+ years of relevant software engineering experience, or a Master’s degree in Computer Science and 7+ years of relevant software engineering experience.
Demonstrated ability to lead multi-stakeholder software projects from needs definition and planning through delivery and support while managing scope, dependencies, and changing constraints.
Demonstrated ability to apply technical judgment across multiple related areas and systems, identify cross-system issues, evaluate alternatives, and guide practical solutions.
Proficiency in one or more modern programming languages and object-oriented design, and experience developing extensible, well-tested, maintainable software.
Experience with version control, code review, automated testing, technical documentation, and collaborative software-development practices.
Experience designing or integrating APIs, databases or other data systems, and production services or workflows.
Effective written and verbal communication skills, including the ability to work productively with scientific, technical, and operational stakeholders.
Ability to learn new scientific and technical domains quickly and work effectively independently and as part of a small, collaborative team.

Nice To Haves

Experience supporting scientific, clinical, bioinformatics, or other data-intensive software applications, including analysis workflows, data repositories, laboratory information systems, enterprise integrations, and large-scale data movement.
Experience with cloud platforms such as Google Cloud Platform (GCP) or Amazon Web Services (AWS); infrastructure as code; CI/CD pipelines; containerized services such as Docker, Apptainer/Singularity, Kubernetes, or Cloud Run; workflow systems such as Nextflow, Snakemake, or WDL with Cromwell or similar engines; cluster job schedulers such as Slurm, LSF, or Grid Engine, or cloud batch services; Python; PostgreSQL and other relational, document-store, or graph databases; GraphQL; and event-driven architectures using tools such as Kafka or Pub/Sub.
Experience applying security and privacy practices to sensitive or regulated data, familiarity with requirements such as HIPAA and NIST SP 800-171, and experience supporting audit-ready operational processes.
Experience with genomics, computational biology, clinical bioinformatics, or high-throughput computing environments.
Experience using AI-enabled development tools or integrating AI capabilities into software workflows, including evaluating output quality, applying data-classification requirements, and protecting against inappropriate disclosure of data.

Responsibilities

Lead cross-functional projects from requirements clarification and scope definition through release and production support, managing technical and operational tradeoffs, dependencies, and delivery planning.
Design, develop, test, deploy, document, and maintain production-quality software, integrations, APIs, data-processing services, and operational tooling.
Build and improve software supporting genomic testing from sample accessioning through laboratory and sequencing workflows, computational analysis, clinical reporting, and secure genomic-data exchange.
Work with scientific, clinical, and operational stakeholders to clarify needs, define requirements, write specifications, and translate them into technical designs, implementation plans, and maintainable software.
Contribute to product requirements and technical direction, and improve recurring workflows and practices across related products and systems.
Partner with Computational Biology, Clinical Bioinformatics, Sequencing Operations, Project Management, Research Computing, and other stakeholders to deliver reliable, responsive services.
Apply engineering practices including version control, code review, automated testing, release management, documentation, observability, data protection, and secure handling of sensitive information.
Analyze technical and operational challenges across related products and systems, evaluate alternatives, and propose practical, cost-effective solutions.
Mentor engineers through design discussions, code reviews, and knowledge sharing; help resolve technical decisions and strengthen shared practices.
Communicate progress, risks, decisions, and delivery expectations clearly; seek relevant expertise and contribute constructively to collective outcomes.
Use approved AI-enabled tools where appropriate to support software development, testing, documentation, and troubleshooting, validating outputs for accuracy, security, privacy, and compliance with NYGC policies.