Computational Protein Design Scientist

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States' security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are dedicated to fostering a culture that values individuals, talents, partnerships, ideas, experiences, and different perspectives, recognizing their importance to the continued success of the Laboratory's mission.

Pay Range

$140,700 - $214,032

$140,700 - $178,392 Annually for the SES.2 level

$168,780 - $214,032 Annually for the SES.3 level

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage.An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.

We have an openingfor a Computational Protein Design Scientist to conduct research advancing our next-generation, machine learning-driven computational pipeline for protein design and optimization of protein-protein interactions. You will join the Center for Predictive Bioresilience (CPB), a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to develop medical countermeasures.

As a member of our multidisciplinary team, you will collaborate with experts in machine learning, molecular simulation, optimization, and protein structure bioinformatics, and interface with experimentalists generating large datasets via novel high-throughput assays. You will leverage in-house computational tools and contribute to the development of new machine learning-based methods for designing and optimizing proteins (e.g., antibodies, immunogens) as therapeutics and vaccines. You will also work closely with the ML team to align current capabilities and co-develop a vision for next-generation protein design models and tools. This position will be in the Computational Engineering Division (CED), within the Engineering Directorate.

This position will be filled at eitherlevel based on knowledge and related experience as assessed by the responsibilities (outlined below) will be assigned if hired at the higher level.

You will

• Collaborate with project scientists and engineers and participate in developing, implementing, and evaluating computational frameworks (e.g., LLMs, Protein Folding, Inverse Folding, All-atom structure prediction and design models) optimized for protein design.
• Contribute to and actively participate in the development and application of analysis methodologies, analyzing data and documenting research through presentations and peer-reviewed publications.
• Support technical activities for new capability development by providing input, recommending enhancements, and developing solutions to moderately complex technical problems using established and innovative methods.
• Contribute to the completion of project milestones, supporting the fulfillment of organizational goals and objectives.
• Contribute to manuscripts and both informal and formal reports and presentations, documenting project activities, methods and implementation techniques, sequences, requirements, and research results.
• Assist in establishing, implementing, reviewing, updating, and maintaining quality standards for project deliverables.
• Routinely interact with technical contacts at sponsor and partner organizations, representing the organization by providing input on specific technical projects.
• Balance multiple projects/tasks and priorities to ensure deadlines are met, working independently with limited direction within the scope of assignments.
• Perform other duties as assigned.

Additional job responsibilities, at the SES.3 level

• Develop, propose, and implement advanced analysis methodologies and collaborate with team in identifying future research directions and proposals that will secure future projects in the field.
• Guide the completion of projects by independently determining the appropriate technical objectives, criteria, and approaches to satisfy and execute project deliverables, leading and overseeing the activities of other personnel, and providing mentoring to less-experienced team members.
• Represent the organization as the primary technical contact on tasks and projects, serving on internal technical/advisory committees, sharing relevant knowledge, providing opinions and recommendations, and exerting influence in developing and achieving project goals.
• Contribute to and influence the development of innovative projects, principles, and ideas in computational protein design.

• Master's degree in Machine Learning, Computational Biology, Statistics, Computer Science, Mathematics, or a related technical field, or the equivalent combination of education and related experience.
• Comprehensive knowledge and experience developing and applying algorithms in one or more of the following machine learning areas: deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning.
• Experience developing and implementing deep learning models and algorithms, and knowledge of and proficiency using modern software libraries such as PyTorch, TensorFlow, or similar, as evidenced by publications or software releases.
• Demonstrated domain knowledge and experience in protein structure machine learning, bioinformatics, and protein structure modeling sufficient to communicate effectively with team members and subject matter experts.
• Proficient verbal and written communication and interpersonal skills and initiative necessary to work independently, collaborate effectively within a multidisciplinary team environment, interact with all levels of personnel, and present and explain technical information to varied audiences.
• Demonstrated ability to prioritize and balance multiple projects and competing demands while maintaining timely and high-quality standards for deliverables.

Additional qualifications at the SES.3 level

• Significant experience and advanced knowledge in developing and applying algorithms in advanced machine learning areas, developing and implementing medium to large-scale deep learning models and algorithms using modern software libraries, and independently developing and executing complex analyses.
• Significant experience and demonstrated ability to lead interdisciplinary teams, set clear expectations, delegate, ensure timely and successful completion of objectives, and influence and provide guidance, advice, and informal mentoring to other personnel and junior team members.
• Demonstrated ability to effectively represent the organization as a primary technical contact, share relevant knowledge, provide opinions and recommendations, exert influence, and contribute to the development of innovative projects, principles, and ideas.

Qualifications We Desire

• PhD in Computational Biology, Computational Bioengineering, Machine Learning, Statistics, Computer Science, Mathematics, or a related field.
• Strong understanding of protein structure bioinformatics and/or protein structure prediction and protein structure datasets.
• Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or running numerical simulations of complex workflows.
• Experience publishing research results in peer-reviewed scientific journals and presenting at conferences and workshops.

#LI-Hybrid

Position Information

This is a Flexible Term appointment, which is for a definite period not to exceed six years.If final candidate is a Career Indefinite employee, Career Indefinite status may be maintained (should funding allow).

Why Lawrence Livermore National Laboratory?

• Included in 2025 Best Places to Work by Glassdoor!
• FlexibleBenefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Our values - visithttps://www.llnl.gov/inclusion/our-values

Security Clearance

None required.However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check. (This process does not apply to foreign nationals.)

National Defense Authorization Act (NDAA)

The 2025 National Defense Authorization Act (NDAA), Section 3112, generally prohibits citizens of China, Russia, Iran and North Korea without dual US citizenship or legal permanent residence from accessing specific non-public areas of national security or nuclear weapons facilities. The restrictions of NDAA Section 3112 apply to this position. To be qualified for this position, Candidates must be eligible to access the Laboratory in compliance with Section 3112.

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 useand/or possession ofmobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area whereyou are not permitted to have a personal and/or laboratory mobile devicein your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.

Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate 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.

CaliforniaPrivacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitlesjob 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.