Computational Bioengineer - Postdoctoral Researcher

Computational Bioengineer - Postdoctoral Researcher

Entry Level | Full-time
Engineering | Livermore, CA | 10/09/2025
Reference #: REF
7601I
Job Code: PDS.
1 Post-Dr Research Staff 1
Organization: Engineering
Position Type: Post Doctoral
Security Clearance: None/Position does not require US citizenship (assignments longer than 179 days require a federal background investigation)
Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
Medical Exam: Not applicable Apply Now

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

$117,900 - $135,060 Annually

This is the lowest to highest salary in good faith 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 opening for a highly motivated Postdoctoral Researcher to conduct research in protein engineering for biomaterials with expertise in machine learning (ML)-driven computational pipeline for protein and polymer design. You will be an integral member of an interdisciplinary team working with computational biologists, and experimental chemists and biologists.

You will leverage computational tools and work to develop new ML-based approaches and tools to design and optimize intrinsically disordered proteins (IDPs) to match desired material properties. You will also work closely with an existing ML and molecular dynamics team to understand current capabilities and jointly develop a vision for development of next generation protein design models and tools.

You will present your work regularly and publish your research and findings, which includes occasional domestic travel. This position is in the Computational Engineering Division (CED), within the Engineering Directorate.

In this role, you will

• Conduct advanced independent research, designing, analyzing, and extending ML-based tools (including large language model-based) for design and optimization of intrinsically disordered proteins (IDPs) and polymers.
• Participate in the development of protein design computational frameworks and analysis tools.
• Collaborate with external partners (Universities, Industry, other National Laboratories) to advance computational biology simulation efforts.
• Prepare complex and detailed progress reports, written analyses, and verbal briefings to support project needs and deadlines and to present research results to sponsors.
• Independently pursue the development of new and innovative research methods relevant to the needs of Laboratory programs and/or external funding agencies.
• Contribute to proposals and statements of work.
• Publish research results in peer-reviewed scientific or technical journals and present results at external conferences, seminars, and/or technical meetings.
• Travel as needed to coordinate with research collaborators and to attend external meetings and conferences.
• Perform other duties as assigned.

• PhD in Machine Learning, Computational Biology, Statistics, Computer Science, Mathematics or a related technical or scientific field, or the equivalent combination of education and related experience.
• Fundamental knowledge and/or experience developing and applying algorithms in several of the following ML areas/tasks: protein sequence to function ML, polymer design ML, deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning, ensemble methods.
• Experience developing and implementing deep learning models and algorithms using modern software libraries such as PyTorch, Tensor Flow, or similar as evidenced through publications or software releases.
• Experience working with proteins and polymers, and domain knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with team members.
• Ability to work independently on defined research projects, as well as a member of a team with a diverse set of scientists, engineers, and other technical and administrative staff.
• Computer skills including programming experience with Python, or C++, and expertise with the UNIX and high-performance computing environments.
• Ability to develop independent research projects as…