Sr. Bioinformatics Engineer at DNAnexus

Prague, Prague, Czechia -

Full Time

Start Date

Immediate

Expiry Date

20 Jul, 26

Salary

0.0

Posted On

21 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Bioinformatics, Computational Biology, Python, Large-scale data processing, WES, WGS, RNA-Seq, LLMs, RAG, Agentic frameworks, Genomics, Multi-omics, Software development, Cloud computing, API integration

Industry

Biotechnology Research

Description

Company Description DNAnexus is the leading cloud-based SaaS company serving the global life science community. DNAnexus’ health informatics platform serves customers across a spectrum of industries — government, biopharmaceutical, clinical diagnostics, healthcare, and academic research in 33 countries with compliant protection of data, privacy, and intellectual property. The platform provides a secure and collaborative environment where genomics, multi-omics, and real world data can be combined with clinical data at scale, providing new insights that can lead to improved diagnostics, new targeted therapies and better patient care. Job Description Team Summary Our platform manages over 125 petabytes of data, processes 50 billion records, and handles 3 billion API calls monthly. With thousands of users depending on us, there is a clear imperative to leverage powerful frontier LLM models and execute on DNAnexus’ Agentic Platform. The platform enables agent-based systems to provide clinical and molecular data workflows in a highly scalable and regulated (PHI) environment. The goal is to enable more intelligent, automated, and reliable execution of complex scientific data processes in the cloud. We’re Looking For A strong background in bioinformatics, computational biology, or a related scientific field. Candidates who can help us nurture this knowledge within the team. Extensive experience with large-scale data processing and analysis pipelines (e.g., WES, WGS, RNA-Seq). Experience developing and integrating with LLMs, RAG systems, or other agentic frameworks for scientific data workflows is highly desirable. Proficiency with relevant bioinformatics tools and databases, we will integrate this to Agentic pipelines Responsibilities Design, develop, and deploy core components of the DNAnexus Agentic Platform, focusing on robust, scalable, and secure systems for genomic and multi-omic data analysis. Integrate advanced LLM capabilities and RAG (Retrieval-Augmented Generation) systems to create intelligent, automated, and auditable scientific workflows. Collaborate with bioinformatics scientists and product managers to translate complex biological research needs into high-quality software solutions. Share bioinformatics knowledge within the team of engineers Stay current with the latest advancements in AI, LLMs, and bioinformatics technology to drive platform innovation. Qualifications 5+ years of professional experience in software development (preferably in Python), preferably in a bioinformatics or life science context. Excellent communication and collaboration skills to work effectively with cross-functional teams (scientists, product managers, engineers). Prior experience or strong interest in AI/ML, particularly integrating LLMs, RAG, or other agentic systems into production workflows is a significant advantage. Additional Information Based in Mountain View, California, DNAnexus is experiencing rapid growth and is searching for the best talent to join our team. We recently completed a $200 million financing round to advance our growth globally to further serve leading healthcare and life science organizations. Key investors include Google Ventures, Perceptive Advisors, Northpond Ventures, TPG Biotech, and Foresite Capital. DNAnexus will provide the necessary support to enable the ideal candidate to work remotely within the Czech Republic. Department: Engineering

Responsibilities

Design, develop, and deploy scalable components for the DNAnexus Agentic Platform to facilitate genomic and multi-omic data analysis. Integrate advanced LLM and RAG systems to create intelligent, automated, and auditable scientific workflows.