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.
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Pay Range: $159324 - $245544 Annually
SES.3 - $159324 - $204636 Annually
SES.4 - $191220 - $245544 Annually
Job Description
We 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.
These positions will be filled at eitherlevel based on knowledge and related experience as assessed by the responsibilities (outlined below) will be assigned if hired at the higher level.
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 level
Lead 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
Masters 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 effectivepresentations 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.
Additional Information
All your information will be kept confidential according to EEO guidelines.
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!
This position requires either no security clearance or a Department of Energy (DOE) L-level or Q-level clearance depending on theparticular assignment.
If you are selected and a security clearance is required wewillinitiate 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 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.
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