Physics World
Computational Protein Design Scientist
Physics World, Livermore, California, United States, 94551
Company DescriptionJoin 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$110,700 - $170,556 Annually$110,700 - $142,128 Annually for the SES.1 level$132,810 - $170,556 Annually for the SES.2 levelPlease note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity.Job DescriptionWe have multiple openings for
Computational Bioengineers
who will conduct research leading to our next-generation, machine learning-driven computational pipeline for protein design and 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.You willWork closely with project scientists and engineers in evaluating and implementing computational frameworks (e.g., large language model-based) optimized for protein design tasks.Contribute to the development of analysis methodologies; analyze data; document research through presentations and peer-reviewed journal articles.Support technical activities for new capability development and provide solutions in solving technical problems of limited complexity using standard techniques and methodologies.Participate in the completion of project milestones and contribute to the development of organizational goals and objectives.Document methods and ensure quality standards for project deliverables.Perform other duties as assigned.Additional job responsibilities, at the SES.2 levelBalance multiple projects/tasks and priorities of customers and partners to ensure deadlines are met, while working independently with minimal direction within scope of the assignment.Support moderately complex research projects that require the creative use of established or innovative methods, working with competing priorities, and implementing advanced research concepts in a multidisciplinary team environment where dedication and deadlines are important to project success.Routinely interact with technical contacts at sponsor and partner organizations.Publish research results in external peer-reviewed scientific journals and participate in conferences and workshops.QualificationsBachelor’s degree in Machine Learning, Computational Biology, Statistics, Computer Science, Mathematics or a related field, or the equivalent combination of education and related experience.Knowledge and experience 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.Experience developing and implementing deep learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidenced through publications or software releases.Experience in protein structure machine learning and domain knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with team members.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.2 levelComprehensive knowledge and broad experience in developing methods to expand knowledge of interrelated fields of advanced protein design application.Comprehensive knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with team members.Proficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information to a variety of audiences.Qualifications We DesireMaster’s degree 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.Additional Information#LI-HybridPosition InformationThis 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 ClearanceThis 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.If no security clearance is required, but 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.)Pre-Employment Drug TestExternal 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 OpportunityWe 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.We invite you to review the Equal Employment Opportunity posters which include EEO is the Law and Pay Transparency Nondiscrimination Provision.Reasonable AccommodationOur 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 NoticeThe 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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Computational Bioengineers
who will conduct research leading to our next-generation, machine learning-driven computational pipeline for protein design and 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.You willWork closely with project scientists and engineers in evaluating and implementing computational frameworks (e.g., large language model-based) optimized for protein design tasks.Contribute to the development of analysis methodologies; analyze data; document research through presentations and peer-reviewed journal articles.Support technical activities for new capability development and provide solutions in solving technical problems of limited complexity using standard techniques and methodologies.Participate in the completion of project milestones and contribute to the development of organizational goals and objectives.Document methods and ensure quality standards for project deliverables.Perform other duties as assigned.Additional job responsibilities, at the SES.2 levelBalance multiple projects/tasks and priorities of customers and partners to ensure deadlines are met, while working independently with minimal direction within scope of the assignment.Support moderately complex research projects that require the creative use of established or innovative methods, working with competing priorities, and implementing advanced research concepts in a multidisciplinary team environment where dedication and deadlines are important to project success.Routinely interact with technical contacts at sponsor and partner organizations.Publish research results in external peer-reviewed scientific journals and participate in conferences and workshops.QualificationsBachelor’s degree in Machine Learning, Computational Biology, Statistics, Computer Science, Mathematics or a related field, or the equivalent combination of education and related experience.Knowledge and experience 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.Experience developing and implementing deep learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidenced through publications or software releases.Experience in protein structure machine learning and domain knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with team members.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.2 levelComprehensive knowledge and broad experience in developing methods to expand knowledge of interrelated fields of advanced protein design application.Comprehensive knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with team members.Proficient verbal and written communication skills necessary to effectively collaborate in a team environment and present and explain technical information to a variety of audiences.Qualifications We DesireMaster’s degree 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.Additional Information#LI-HybridPosition InformationThis 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 ClearanceThis 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.If no security clearance is required, but 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.)Pre-Employment Drug TestExternal 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 OpportunityWe 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.We invite you to review the Equal Employment Opportunity posters which include EEO is the Law and Pay Transparency Nondiscrimination Provision.Reasonable AccommodationOur 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 NoticeThe 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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