Bioinformatician

How You'll Make an Impact

We are seeking a collaborative Bioinformatician to design and develop a next-generation scientific workflow platform that integrates sequencing data, synthetic biology expertise, public biological knowledge, and AI-enabled analytics. This role will translate scientific and operational requirements into scalable, reusable workflows, analytical services, and self-service capabilities for plasmid and viral vector applications. Working across scientific, laboratory, software, quality, product, and client-facing teams, the successful candidate will establish robust platform architecture, automate data processing and interpretation, and deliver validated solutions that improve consistency, scalability, traceability, and scientific decision-making.

Responsibilities

• Design and implement reusable algorithms, services, and workflow components to evaluate sequence designs, construct risks, manufacturability, and functional performance.
• Build configurable analytical frameworks that integrate public scientific resources, proprietary algorithms, and standardized decision rules to support sequence and vector design workflows.
• Develop customer-facing analytics, visualizations, APIs, and self-service tools that expose platform capabilities and turn biological data into actionable insights and recommendations.
• Architect, develop, validate, and maintain scalable workflows for long-read, short-read, Sanger, and hybrid sequencing applications, including automated quality control, assembly, variant analysis, exception handling, and results delivery.
• Apply AI and machine learning to automate workflow orchestration, data interpretation, quality assessment, troubleshooting, summarization, and continuous platform improvement.
• Partner across informatics, laboratory, quality, and commercial teams to define requirements, prioritize capabilities, integrate instruments and systems, and scale scientific workflows into supported digital products.
• Create and maintain platform architecture documentation, workflow specifications, validation records, operating procedures, release documentation, and user guidance.
• Maintain expertise in workflow architecture, sequencing technologies, vector biology, synthetic biology, AI, bioinformatics, and scientific software engineering.

Required Qualifications

• PhD in bioinformatics, computational biology, genomics, molecular biology, biostatistics, computer science, or a related field preferred; candidates with a master's degree and relevant experience may also be considered.
• Experience designing and developing automated NGS and long-read analysis workflows, including assembly, polishing, structural variant analysis, quality control, exception handling, and support for FASTQ, FASTA, BAM/CRAM, VCF, BED, GFF/GTF, and GenBank formats.
• Demonstrated experience translating plasmid or viral vector design principles into reusable computational rules, algorithms, or platform capabilities that evaluate expression, stability, packaging, delivery, manufacturability, and other functional outcomes.
• Strong understanding of molecular biology, genetics, cloning, gene regulation, plasmid and viral vector architecture, and sequence-to-function relationships.
• Working knowledge of both short-read and long-read sequencing technologies, including their error profiles, strengths, limitations, and appropriate analysis strategies.
• Proficiency in Linux and programming or scripting languages, preferably Python, R, or Bash.
• Experience with version control, collaborative code review, automated testing, reproducible builds, and clear technical documentation for production workflows.
• Strong analytical, troubleshooting, organizational, and written and verbal communication skills.

Preferred Qualifications

• Experience developing production-quality scientific software, workflow platforms, pipelines, APIs, or analytical services and integrating public datasets, biological databases, laboratory systems, and open-source scientific tools.
• Hands-on software development experience across the software development life cycle, including requirements definition, architecture, implementation, testing, documentation, deployment, release management, monitoring, and maintenance.
• Experience designing and optimizing AAV, lentiviral, CRISPR, microbial, or other engineered vector systems, with evidence of linking design choices to experimental performance.
• Experience with modern AI/ML methods and their application to biological data.