Additionalinformation

Additional Information

#LI-Hybrid

Position Information

This is a one-year Academic Graduate Appointee, open to those who have been awarded a degree at the time of the employment offer.

Why Lawrence Livermore National Laboratory?

• Included in 2026 Best Places to Work by Glassdoor!
• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Our values - visit https://www.llnl.gov/inclusion/our-values

Security Clearance

This position requires a Department of Energy (DOE) Q-level clearance. If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship.

Pre-Employment Drug Test

External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

Wireless and Medical Devices

Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.

If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.

How to identify fake job advertisements

Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under “Find Your Job” of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.

To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf

Equal Employment Opportunity

We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

Reasonable Accommodation

Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.

Companydescription

Company Description

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

Jobdescription

Job Description

Note: This is a one-year Academic Graduate Appointee with the possibility of extension to a maximum of two years.

We're looking for an Academic Graduate Appointee to support interdisciplinary research in computational chemistry, molecular design, data wrangling, machine learning, and high-performance data analysis by applying established computational methods to defined scientific data-analysis assignments under guidance. The role will assist with developing and maintaining reliable, reproducible workflows for managing and analyzing large-scale chemical, molecular, and biological datasets. Responsibilities include supporting the integration of simulation, experimental, and database data; generating molecular descriptors; contributing to automated ETL pipelines; and creating tools to identify meaningful patterns. The successful candidate will collaborate across computational, experimental, and engineering disciplines to support molecular dynamics, small-molecule inhibitor discovery, AI-enabled drug discovery, and reproducible research methods. Assignments will provide opportunities to develop technical skills and professional experience under the guidance of experienced staff. This position is in the Biochemical and Biophysical Systems Group in the Biosciences and Biotechnology Division within the Physical and Life Sciences Directorate.

Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.

You will

• Collaborate with computational chemists, computational biologists, experimental scientists, computer scientists, and engineers to support defined computational and data analytics assignments under guidance.
• Assist in implementing and maintaining data-wrangling workflows to collect, ingest, clean, transform, standardize, and validate chemical, molecular, simulation, and biological datasets using established methods.
• Contribute to the development and testing of ETL pipelines for high-volume scientific data, including molecular structures, simulation outputs, and molecular descriptors.
• Assist with data from computational databases, molecular dynamics simulations, high-throughput experiments, and external sources.
• Identify data-quality issues, including missing, duplicated, inconsistent, incomplete, or anomalous data using established data-quality practices and escalate nonroutine issues to senior team members.
• Develop basic data visualizations and analysis toolsto identify chemical, structural, and biological trends while maintaining well-documented, version-controlled, and reproducible code for scientific data analysis.
• Present research progress and technical results to internal team members and, as appropriate, collaborators, and other scientific audiences with guidance from senior staff while maintaining high-quality deliverables.
• Meet all respective deadlines.
• Perform other duties as assigned.

Qualifications

• Ability to secure and maintain a U.S. Department of Energy Q-level security clearance, which requires U.S. citizenship and a federal background investigation.
• Bachelor's and/or master's degree in computational chemistry, bioengineering, biomedical engineering, computational biology, bioinformatics, data science, computer science, or a related field.
• Coursework, research, internship, or project experience usingLinux-based ETL workflows for data ingestion, cleaning, transformation, standardization, and validation of large-scale chemical, molecular, biological, simulation, or experimental datasets.
• Familiarity with data-quality checks, validation procedures, and reproducible data-processing workflows particularly those involved with generating, transforming, and analyzing molecular descriptors, chemical features, or other scientific data representations.
• Foundational knowledge of one or more of the following: computational chemistry, molecular modeling, molecular dynamics, molecular design, or small-molecule discovery.
• Familiarity with, or coursework/project experience in,high-performance, parallel, or distributed data processing systems and practices, including Slurm, Apache Spark, MPI, OpenMP, CUDA, or multi-node computing.
• Ability to write clear, maintainable, and well-documented code with proficient verbal and written communication skills necessary to document analyses and present technical information.
• Ability to work collaboratively with computational chemists, biologists, experimental scientists, engineers, and other technical personnel on multiple projects, prioritize competing demands, and maintain high-quality standards for deliverables.

Qualifications We Desire

• Experience identifying and addressing data issues part of larger computational ETL workflows, including missing values, inconsistent schemas, duplicate records, incompatible formats, and anomalous observations.
• Experience or coursework in website management, development, and optimization including relevant frameworks, packages, protocols, and features.
• Experience generating, transforming, curating, or analyzing molecular descriptors, chemical structures, simulation outputs, or biological features through coursework, research, or projects.
• Familiarity with workflow automation, batch processing, version control, and documentation for scientific computing environments.
• Working knowledge of computational chemistry, molecular modeling, molecular dynamics simulations, molecular design, or small-molecule discovery to collaborate with specialists in the field.

Pay Range

$6,748 - $7,718 Monthly

Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity.

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