Computational Protein Design Scientist

Company Description

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.

$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 opening for 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 either level based on knowledge and related experience as assessed by the responsibilities (outlined below) will be assigned if hired at the higher level.

• 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 ensure deadlines are met, working independently with limited direction within the scope of assignments;
• Perform other duties as assigned.

• Develop, propose, and implement advanced analysis methodologies and collaborate with team in identifying future…