Machine Learning (ML) Bioengineer

LLNL•Livermore, CA

1d•Hybrid

About The Position

We have an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and public health missions. This position will be in the Computational Engineering Division (CED), within the Engineering Directorate, matrixed to the Bioresilience Incubator. As a member of our multidisciplinary team, you will collaborate with experts in machine learning, molecular simulation, optimization, and bioinformatics, and interface with experimentalists generating large datasets via novel high-throughput assays. You will leverage in-house computational tools and contribute to the design, training, and evaluation of new machine learning-based methods. Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week. 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.

Requirements

Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
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.
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 using modern software libraries such as PyTorch, TensorFlow, or similar, as evidenced by publications or software releases.
Experience with high-performance computing, multi-node, multi-GPU, distributed training.
Comprehensive knowledge in protein and genome language models sufficient to communicate effectively with team members and subject matter experts.
Proficient verbal and written communication skills necessary to collaborate within a team environment and present technical information to varied audiences.
Effective interpersonal skills and initiative necessary to interact with all levels of personnel and work independently in a collaborative, multidisciplinary team environment.
Demonstrated ability to balance multiple projects and prioritize competing demands while maintaining high-quality standards for deliverables.
Advanced knowledge and experience in developing and applying algorithms in machine learning areas.
Significant experience developing and implementing medium to large-scale deep learning models and algorithms using modern software libraries.
Demonstrated ability to provide guidance and informal mentoring to other personnel and junior team members.
Advanced verbal and written communication skills necessary to effectively collaborate in a multidisciplinary team and present technical information to a variety of audiences.
Demonstrated ability to represent the organization as a primary technical contact and to contribute to the development of innovative projects, principles, and ideas.

Nice To Haves

PhD in Computational Biology, Computational Bioengineering, Machine Learning, Statistics, Computer Science, Mathematics, or a related field.
Strong understanding of protein and genome language models and datasets.
Experience publishing research results in peer-reviewed scientific journals and presenting at conferences and workshops.
Experience with GPU programming and running complex workflows.

Responsibilities

Collaborate with project scientists and engineers to develop, implement, and evaluate computational frameworks and models.
Contribute to the development and application of advanced analysis methodologies; analyze data; document research through presentations and peer-reviewed publications.
Support technical activities for new capability development and provide solutions to moderately complex to complex technical problems using established and innovative methods.
Contribute to the completion of project milestones, influencing the development of organizational goals and objectives.
Establish, implement, and maintain quality standards for project deliverables.
Contribute to briefings and presentations documenting project activities and research results.
Routinely interact with technical contacts at sponsor and partner organizations; represent the organization on specific technical projects.
Participate in the development of future research directions and proposals to secure ongoing projects in computational protein design.
Balance multiple projects/tasks and priorities to ensure deadlines are met, working independently with minimal direction within the scope of assignments.
Perform other duties as assigned.
Determine, propose, and implement advanced analysis methodologies and contribute to identifying future research directions and proposals that will secure future projects in the field.
Guide the completion of projects and influence the development of organizational goals and objectives.
Lead the development of briefings and presentations documenting to project activities and research results.
Represent the organization as the primary technical contact on tasks and projects, serving on internal technical/advisory committees and potentially on external committees.
Oversee the activities of other personnel, providing informal mentoring and guidance to less-experienced team members.
Contribute to and influence the development of innovative projects, principles, and ideas in computational protein design.