Lawrence Livermore National Laboratory
Computational Protein Design Scientist
Lawrence Livermore National Laboratory, Livermore, California, United States, 94551
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 committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is important for continued success of the Laboratory’s mission.Pay Range:
$159,324 - $245,544 AnnuallySES.3 - $159,324 - $204,636 AnnuallySES.4 - $191,220 - $245,544 AnnuallyWe have multiple openings for
Computational Bioengineers
who will conduct research leading to our next-generation, machine learning-driven computational pipeline for optimizing protein-protein interactions as part of the Center for Predictive Bioresilience (CPB). CPB is an exciting and fast-paced engineering center combining predictive computational modeling, machine learning, and experimental biology to develop medical countermeasures.You will work within a multi-disciplinary team with computational expertise in machine learning (ML), molecular simulation, optimization, and protein structure bioinformatics, and interface with our experimental team generating large datasets with novel high throughput assays aimed at informing predictive model development. You will leverage in-house computational tools and work to develop new machine-learning-based approaches and tools to design and optimize proteins (antibodies, immunogens, etc.) as therapeutics and vaccines. You will also work closely with an existing ML team to understand current capabilities and jointly develop a vision for development of next generation protein design models and tools. You will be team-oriented and have experience working in a team environment to achieve common goals. These positions will be in the Computational Engineering Division (CED), within the Engineering Directorate, matrixed to the Center for Predictive Bioresilience.In this role you will:Collaborate and work closely with project scientists and engineers in evaluating and implementing computational frameworks (e.g., large language model-based) optimized for protein design tasks.Determine, propose, and implement advanced analysis methodologies; analyze data; document research through presentations and peer-reviewed journal articles; and contribute to identifying future research directions and proposals that will secure future projects in the field.Support technical activities for new capability development and complex technical problem solving.Guide the completion of projects and contribute to and influence the development of organizational goals and objectives.Establish, implement, maintain, and ensure quality standards for project deliverables.Perform other duties as assigned.Additional job responsibilities, at the SES.4 levelLead the analysis, development, modification, and utilization of a variety of innovative and diverse machine-learning-based approaches, methods, techniques, and evaluation criteria to provide solutions to highly complex problems.Provide strategic technical advice and solutions, serve as a primary technical point of contact, and participate in the development of new program business, working with management, partners, and external stakeholders.Direct technical tasks and projects, set broad vision and strategy, influence the direction, and contribute to the development of innovative projects, principles, and ideas.Lead, guide, and mentor other staff, and train newly hired staff and junior members.Qualifications:Master’s degree in Machine Learning, Computational Biology, Statistics, Computer Science, Mathematics or a related field, or the equivalent combination of education and related experience.Significant experience and advanced knowledge of developing and applying algorithms in one or more of the following machine learning areas/tasks: deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning, ensemble methods, scalable online estimation, and probabilistic graphical models.Significant experience developing and implementing medium to large scale deep learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidenced through publications or software releases.Domain knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with subject matter experts, and to identify novel, impactful applications of machine learning.Advanced verbal and written communication skills as reflected in effective presentations and explanations at seminars, meetings and/or teaching lectures.Effective interpersonal skills and initiative necessary to interact with all levels of personnel with the ability to work independently in a collaborative, multidisciplinary team environment.Additional qualifications at the SES.4 level:Demonstrated ability to provide technical leadership of multidisciplinary teams in fields related to machine learning, such as mentorship or managing teams.Significant experience and subject matter expert knowledge in developing innovative methods to expand knowledge of interrelated fields of highly advanced protein design application.Expert level skills in the application and development of industry best practices, principles, theories, concepts, and techniques.Expert communication, facilitation, and collaboration skills are necessary to present, explain, and advise external sponsors.Qualifications We Desire:Ability to secure and maintain a U.S. DOE Q-level security clearance, which requires U.S. Citizenship.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.Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflow.Position Information:This is a Career Indefinite position, open to Lab employees and external candidates.Why Lawrence Livermore National Laboratory?Included in 2024 Best Places to Work by Glassdoor!Flexible Benefits Package401(k)Relocation AssistanceEducation Reimbursement ProgramFlexible schedules (*depending on project needs)Inclusion, Diversity, Equity and Accountability (IDEA) - visit https://www.llnl.gov/diversityOur core beliefs - visit https://www.llnl.gov/diversity/our-valuesEmployee engagement - visit https://www.llnl.gov/diversity/employee-engagementSecurity Clearance:This position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on the particular assignment.If you are selected and a security clearance is required, 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. L and Q-level clearances require 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.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.
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$159,324 - $245,544 AnnuallySES.3 - $159,324 - $204,636 AnnuallySES.4 - $191,220 - $245,544 AnnuallyWe have multiple openings for
Computational Bioengineers
who will conduct research leading to our next-generation, machine learning-driven computational pipeline for optimizing protein-protein interactions as part of the Center for Predictive Bioresilience (CPB). CPB is an exciting and fast-paced engineering center combining predictive computational modeling, machine learning, and experimental biology to develop medical countermeasures.You will work within a multi-disciplinary team with computational expertise in machine learning (ML), molecular simulation, optimization, and protein structure bioinformatics, and interface with our experimental team generating large datasets with novel high throughput assays aimed at informing predictive model development. You will leverage in-house computational tools and work to develop new machine-learning-based approaches and tools to design and optimize proteins (antibodies, immunogens, etc.) as therapeutics and vaccines. You will also work closely with an existing ML team to understand current capabilities and jointly develop a vision for development of next generation protein design models and tools. You will be team-oriented and have experience working in a team environment to achieve common goals. These positions will be in the Computational Engineering Division (CED), within the Engineering Directorate, matrixed to the Center for Predictive Bioresilience.In this role you will:Collaborate and work closely with project scientists and engineers in evaluating and implementing computational frameworks (e.g., large language model-based) optimized for protein design tasks.Determine, propose, and implement advanced analysis methodologies; analyze data; document research through presentations and peer-reviewed journal articles; and contribute to identifying future research directions and proposals that will secure future projects in the field.Support technical activities for new capability development and complex technical problem solving.Guide the completion of projects and contribute to and influence the development of organizational goals and objectives.Establish, implement, maintain, and ensure quality standards for project deliverables.Perform other duties as assigned.Additional job responsibilities, at the SES.4 levelLead the analysis, development, modification, and utilization of a variety of innovative and diverse machine-learning-based approaches, methods, techniques, and evaluation criteria to provide solutions to highly complex problems.Provide strategic technical advice and solutions, serve as a primary technical point of contact, and participate in the development of new program business, working with management, partners, and external stakeholders.Direct technical tasks and projects, set broad vision and strategy, influence the direction, and contribute to the development of innovative projects, principles, and ideas.Lead, guide, and mentor other staff, and train newly hired staff and junior members.Qualifications:Master’s degree in Machine Learning, Computational Biology, Statistics, Computer Science, Mathematics or a related field, or the equivalent combination of education and related experience.Significant experience and advanced knowledge of developing and applying algorithms in one or more of the following machine learning areas/tasks: deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning, ensemble methods, scalable online estimation, and probabilistic graphical models.Significant experience developing and implementing medium to large scale deep learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidenced through publications or software releases.Domain knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with subject matter experts, and to identify novel, impactful applications of machine learning.Advanced verbal and written communication skills as reflected in effective presentations and explanations at seminars, meetings and/or teaching lectures.Effective interpersonal skills and initiative necessary to interact with all levels of personnel with the ability to work independently in a collaborative, multidisciplinary team environment.Additional qualifications at the SES.4 level:Demonstrated ability to provide technical leadership of multidisciplinary teams in fields related to machine learning, such as mentorship or managing teams.Significant experience and subject matter expert knowledge in developing innovative methods to expand knowledge of interrelated fields of highly advanced protein design application.Expert level skills in the application and development of industry best practices, principles, theories, concepts, and techniques.Expert communication, facilitation, and collaboration skills are necessary to present, explain, and advise external sponsors.Qualifications We Desire:Ability to secure and maintain a U.S. DOE Q-level security clearance, which requires U.S. Citizenship.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.Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflow.Position Information:This is a Career Indefinite position, open to Lab employees and external candidates.Why Lawrence Livermore National Laboratory?Included in 2024 Best Places to Work by Glassdoor!Flexible Benefits Package401(k)Relocation AssistanceEducation Reimbursement ProgramFlexible schedules (*depending on project needs)Inclusion, Diversity, Equity and Accountability (IDEA) - visit https://www.llnl.gov/diversityOur core beliefs - visit https://www.llnl.gov/diversity/our-valuesEmployee engagement - visit https://www.llnl.gov/diversity/employee-engagementSecurity Clearance:This position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on the particular assignment.If you are selected and a security clearance is required, 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. L and Q-level clearances require 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.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.
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