Lawrence Livermore National Laboratory (LLNL) is hiring a Computational Protein Design Scientist to conduct research advancing a next-generation, machine learning-driven computational pipeline for protein design and optimization. You will collaborate with a multidisciplinary team of experts in machine learning, molecular simulation, optimization, and protein structure bioinformatics, and interface with experimentalists generating large datasets via novel high-throughput assays.
What You'll Do
- Collaborate with project scientists and engineers to develop, implement, and evaluate computational frameworks optimized for protein design, including LLMs, protein folding, inverse folding, and all-atom structure prediction and design models.
- 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.
- Balance multiple projects and priorities to ensure deadlines are met, working independently with limited direction within the scope of assignments.
- Develop, propose, and implement advanced analysis methodologies and collaborate with the team in identifying future research directions and proposals (SES.3 level).
- Guide the completion of projects by independently determining technical objectives and criteria, leading and overseeing the activities of other personnel, and providing mentoring to less-experienced team members (SES.3 level).
- Represent the organization as the primary technical contact on tasks and projects, serving on internal technical/advisory committees, sharing relevant knowledge, and exerting influence in developing project goals (SES.3 level).
What We're Looking For
- 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 or TensorFlow.
- Demonstrated domain knowledge and experience in protein structure machine learning, bioinformatics, and protein structure modeling.
- Proficient verbal and written communication and interpersonal skills and initiative necessary to work independently and collaborate effectively within a multidisciplinary team environment.
- Demonstrated ability to prioritize and balance multiple projects and competing demands while maintaining timely and high-quality standards for deliverables.
- 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 independently developing and executing complex analyses (SES.3 level).
- Significant experience and demonstrated ability to lead interdisciplinary teams, set clear expectations, delegate, ensure timely completion of objectives, and influence and provide guidance to other personnel (SES.3 level).
- 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 (SES.3 level).
Nice to Have
- 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.
Technical Stack
- PyTorch
- TensorFlow
- High-performance computing
- GPU programming
- Parallel programming
- Cloud computing
Team & Environment
You will be a member of a multidisciplinary team within the Center for Predictive Bioresilience (CPB). This position is in the Computational Engineering Division (CED), within the Engineering Directorate.
Benefits & Compensation
- Compensation: $146,340 - $185,544 Annually for the SES.2 level; $175,530 - $222,564 Annually for the SES.3 level.
- Flexible Benefits Package
- 401(k)
- Relocation Assistance
- Education Reimbursement Program
- Flexible schedules (depending on project needs)
Work Mode
This position is onsite.
Lawrence Livermore National Laboratory is an equal opportunity employer and values a diverse workforce.
