Bioinfomatics Scientist

LLNL•Livermore, CA

5d•Onsite

About The Position

Lawrence Livermore National Laboratory (LLNL) is seeking a Bioinformatics Scientist to conduct research, training, and evaluation of next-generation bioinformatics and data management tools. As a member of a multidisciplinary team, you will collaborate with experts in artificial intelligence, machine learning, molecular biology, and software engineering. You will interface with experimentalists generating large datasets to explore opportunities for advancing scientific capabilities in the detection and surveillance of novel and emerging pathogens. This includes developing new methods, tools, and algorithms for metagenomic sequence data analysis and pathogen characterization, as well as working with multiomic datasets and applying state-of-the-art machine learning models for integrated data analysis and prediction. This position is within the Advanced Biotechnologies Integration group in the Biosciences and Biotechnology Division (BBTD).

Requirements

Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
Master’s degree in Computational Biology, Biophysics, Computational Bioengineering, Machine Learning, Statistics, Computer Science, Mathematics, or equivalent combination of education and related technical experience.
Significant experience in metagenomics, bacterial genomics, and next-generation sequencing (NGS) analysis.
Advanced experience in scientific computing tools such as C/C++, Python, R, MATLAB, Nextflow, PostgreSQL, MySQL, MongoDB, and HPC schedulers.
Significant experience developing and deploying end-to-end workflows for pathogen detection, taxonomic classification, genome assembly, subtyping, and metagenomic analysis.
Proven ability to translate mission needs into scalable, reusable analytical frameworks and tools, with experience supporting both research and operational bioinformatics activities.
Advanced experience in large data management such as metadata schema development, data cataloging, database development, and integration with pipelines, AI/ML models and workflows.
Significant experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or running numerical simulations of complex workflows.
Advanced verbal and written communication skills necessary to collaborate within a team environment and present technical information to varied audiences.
Effective interpersonal skills and initiative necessary to interact with all levels of personnel and work independently in a collaborative, multidisciplinary team environment.
Advanced ability to balance multiple projects, prioritize competing demands while maintaining high-quality standards, and provide guidance and informal mentoring to other personnel and junior team members.
Ability to represent the organization as a primary technical contact and to contribute to the development of innovative projects, principles, and ideas.

Nice To Haves

PhD in Computational Biology, Biophysics, Computational Bioengineering, Machine Learning, Statistics, Computer Science, Mathematics, or a related field.
Significant knowledge in gene calling, biological process modeling, or application of protein and genome language models sufficient to communicate effectively with team members and subject matter experts.
Experience and advanced knowledge in developing and applying algorithms in advanced machine learning or cell modeling.
Significant experience developing and implementing machine learning models such as deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning.

Responsibilities

Analyze metagenomic datasets from environmental (e.g., wastewater, aerosol) and clinical (nasal swab, saliva, etc.) samples to determine taxonomy and function.
Research, develop, and implement computational and statistical techniques for analyzing novel and unknown sequences.
Develop simulation-based and agentic tools for hypothesis generation and in silico testing.
Contribute to biological data management such as metadata schemas, databases, data catalogs, and integrated workflows.
Contribute to and influence the development of innovative projects, principles, and ideas in biosurveillance and biodetection.
Interact with technical contacts at sponsor and partner organizations routinely; represent the organization on specific technical projects.
Determine, propose, and implement advanced analysis methodologies and contribute to identifying future research directions and proposals that will secure future projects in the field.
Contribute to the completion of project milestones, balance multiple projects/tasks and priorities to ensure deadlines are met, working independently with minimal direction within the scope of assignments.
Lead the development of manuscripts and presentations documenting project activities and research results.
Oversee the activities of other personnel, providing informal mentoring and guidance to less experienced team members.
Perform other duties as assigned.