What are the responsibilities and job description for the Aikium Inc. | Deep Learning Scientist, Protein Design position at Aikium Inc.?
Computational Protein Design Virtuoso WantedAikium - Where Passion Meets Purpose
Aikium Inc. is revolutionizing AI-driven protein engineering, shifting the paradigm from cherry-picked top-10 evaluation to ultra-large design-library screening. Our groundbreaking Yotta Display platform enables the synthesis and experimental screening of trillion-protein libraries, pushing the boundaries of what's possible in protein design. Our internal pipelines focus on SeqRs, our patented non-antibody disorder binding protein scaffold for targeting multi-pass membrane proteins. Expansion to several scaffolds and diverse targets is under way to support our growing internal pipeline and numerous external partnerships.
We're seeking a visionary leader to spearhead the development and application of cutting-edge deep learning approaches for engineering all therapeutically relevant protein scaffolds.
Your ExpertiseAdvanced Qualifications : Ph.D. in the computational sciences2 years of hands-on deep learning experience in protein engineering / designProven track record in developing one or more of the following : Large Language Models over biological sequence dataGeometric deep learning over protein structuresDiffusion-based models for any class of biomolecules
Solid foundations : Comprehensive knowledge of computational methods, toolkits, and databases in protein sciencesStrong understanding of therapeutic protein development objectivesFamiliarity with traditional bioinformatics, Next Generation Sequencing and molecular dynamics simulationExposure to data from different types of experiments for prosecuting protein-protein interactions
Special Attributes : Critical thinking and advanced analytical skillsPassion for tackling complex, seemingly intractable problemsPragmatic approach to meeting milestones and getting things done!
Why Aikium?Be an early driver at a field-defining startup chasing the hard problemsAccess to one-of-a-kind synthetic biology platform that can generate labeled data at scaleFounded by an experienced and grounded multi-disciplinary teamCompetitive compensation with generous stock options