The Generative Unconstrained Intelligent Drug Engineering (GUIDE) project has an opening for a Senior bioengineer who is responsible for the efficient, smooth, and effective management of a team and simulation- and machine-learning-driven computational pipeline to redesign antibodies. GUIDE is an exciting and fast-paced program combining predictive computational modeling, machine learning, and experimental biology to develop medical countermeasures for the Department of Defense (DoD). The ideal candidate will have a breadth of protein engineering experience to manage a multi-disciplinary team with expertise in machine learning, molecular simulation, optimization, and structural and protein biology. The candidate will apply in-house computational tools in rigorous campaigns to redesign specific antibodies so that they are more potent, broadly neutralizing and/or developable as a prophylactic or therapeutic antibodies.
The candidate will elicit highly technical information required to formulate antibody redesign goals; to facilitate the creation and joint understanding of these goals via a network of colleagues with differing technical expertise; to communicate and ensure ongoing consensus on these goals; and to continually guide alignment of effort to them. The candidate will manage all aspects of the computational redesign campaign, and, as such, requires deep technical expertise in protein and structural biology/bioegineering or machine learning and simulation tools, and a willingness to gain complementary expertise.
The candidate needs to be comfortable with a flexible technical approach, willing to re-compose existing tools and workflows to execute the job at hand, and to lead others toward the execution of the work.
The candidate must be team-oriented and have leadership experience and resilience to drive the work forward while maintaining a balanced view that serves the needs of the GUIDE program and its durable capabilities.
You will
- Research, analyze, develop, document, and execute a technical plan for each antibody redesign campaign, including determination of appropriate data, tools, and team members to support the effort.
- Manage all aspects of the computational redesign campaign from start-to-finish.
- Actively lead project scientists and engineers in defining, planning, and formulating experimental, modeling, and simulation efforts for complex antibody redesign campaigns.
- Propose and implement advanced analysis methodologies, analyze data, and 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.
- Foster collaboration between computational and biological staff to improve the speed and accuracy of computational antibody redesign campaigns.
- Direct technical activities, as needed, in support of new capability development and technical problem solving.
- Establish ,maintain and ensure quality standards for project deliverables and guide project teams through antibody redesign processes.
- Perform other duties as assigned.
- Ability to secure and maintain a U.S. DOE Q-level security clearance, which requires U.S. Citizenship.
- Advanced degree in Computational Biology, Machine Learning, Statistics, Computer Science, Mathematics or a related field.
- Demonstrated technical leadership in leading multidisciplinary teams in fields related to machine learning, such as mentorship or team management.
- Expert verbal and written communication skills as reflected in effective presentations at seminars, meetings and/or teaching lectures.
- Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.
Additional Qualifications We Desire
- A good-to-strong understanding of bioinformatics, protein biology, antibody development, and pathogen science, especially virology
- Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflow