Principal Software Engineer

New York Genome Center•New York, NY

8d•$183,000 - $214,000

About The Position

At the New York Genome Center (NYGC), the Principal Software Engineer is primarily an individual contributor who sets technical direction and leads the design and delivery of high-impact software across interconnected products and systems. The role applies engineering judgment to challenges with unclear or undefined requirements spanning departments; builds and evolves reliable, secure, maintainable solutions; and remains hands-on in architecture, implementation, and production operations. Working across scientific, clinical, and operational teams, the Principal Software Engineer establishes durable engineering practices, guides technical decisions, and mentors engineers while partnering with engineering leaders on technical approach and delivery priorities. Supervisory responsibility for one or two direct reports may be assigned based on organizational needs and the individual's demonstrated experience and skill set. The role supports systems spanning genomic testing, from sample accessioning through laboratory and sequencing workflows, computational analysis, clinical reporting, and secure genomic-data exchange. It also guides responsible adoption of AI-enabled tools and capabilities in accordance with NYGC approval, security, privacy, and governance requirements. NYGC is an independent, nonprofit, academic research organization dedicated to advancing genomic research. It is located in Lower Manhattan and works closely with leading academic medical centers and research universities in the New York region. The pay range is $183,000–$214,000 per year; base pay may vary depending on job-related knowledge, skills, and experience.

Requirements

Bachelor’s degree in Computer Science and 12 or more years of relevant software engineering experience, or a Master’s degree in Computer Science and 10 or more years of relevant software engineering experience.
Demonstrated success leading high-impact, cross-team software initiatives from problem definition and specification through delivery and ongoing production support.
Software architecture and engineering expertise across multiple related technical areas, with sound judgment in decisions affecting interconnected systems.
Proficiency in one or more modern programming languages and object-oriented design, and a record of delivering extensible, well-tested, maintainable production software.
Experience designing or integrating APIs, databases or other data systems, and production services or workflows.
Demonstrated ability to improve engineering quality, reliability, security, and operational practices across a team or group of related systems.
Strong written and verbal communication skills, including explaining technical tradeoffs and influencing decisions with scientific, technical, operational, and leadership stakeholders.
Ability to work independently on broad technical problems, collaborate across functions, and develop expertise in new scientific and technical domains.

Nice To Haves

Experience supporting scientific, clinical, bioinformatics, or other data-intensive software applications across multiple products or systems, 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 orchestration tools such as Nextflow, Snakemake, WDL with Cromwell or similar engines, or Airflow; durable execution frameworks; cluster job schedulers such as Slurm, LSF, or Grid Engine, or cloud batch services; Python; PostgreSQL and other relational, document-store, graph, or analytical databases; GraphQL; FAIR and AI-ready data, including RDF; or event-driven architectures using tools such as Kafka or Pub/Sub.
Experience with genomics, computational biology, clinical bioinformatics, sequencing operations, or high-throughput computing environments.
Experience establishing and advancing security and privacy practices for sensitive or regulated data across multiple related systems, aligning practices with requirements such as HIPAA and NIST SP 800-171, and designing audit-ready processes and operational controls.
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.
Experience evaluating or integrating AI tools or capabilities in production systems within established approval and governance processes, including validation, security, privacy, and operational support.

Responsibilities

Set technical direction and architecture across the software product suite, services, data systems, and integrations, aligning choices with organizational needs, system constraints, and long-term maintainability.
Lead high-impact, ambiguous cross-functional projects from discovery through release and production support; establish the technical execution approach and manage architecture, risk, dependencies, and delivery tradeoffs.
Design, develop, review, test, deploy, document, and maintain production software, APIs, data-processing services, scientific workflows, and operational tooling.
Lead the design and evolution of interconnected operational systems supporting genomic testing, and improve integrations, reliability, and observability so systems can operate at scale.
Work with scientific, clinical, and operational stakeholders to clarify needs, establish specifications, translate them into scalable technical approaches and implementation plans, explain tradeoffs, and build agreement.
Partner with lab supervisors, clinical directors, computational biologists, Research Computing, and working groups to deliver dependable services and improve cross-system workflows.
Establish and advance shared engineering practices for architecture, version control, code review, automated testing, release management, documentation, observability, data protection, and secure handling of sensitive information.
Guide modernization and technical-debt work, simplifying systems, improving reliability and performance, and reducing operational burden through incremental, supportable changes.
Diagnose system-wide quality, performance, capacity, integration, and production challenges; bring together relevant expertise, evaluate options, and drive root-cause solutions across system boundaries.
Mentor and coach engineers through design discussions, code reviews, and knowledge sharing; strengthen technical capability and shared practices. Supervise, coach, and provide performance feedback to direct reports when assigned.
Communicate technical options, decisions, dependencies, risks, and progress to the Software Engineering team, stakeholders, and organizational leaders; provide evidence-based recommendations for roadmaps and cross-functional initiatives.
Use approved AI-enabled tools where appropriate to support software development, testing, documentation, and troubleshooting; validate outputs for accuracy, security, privacy, and policy compliance.
Guide technical evaluation and responsible adoption of AI tools and AI-enabled capabilities across products and workflows, and establish practices for validation, security, privacy, compliance, reliability, and operational support.