About the roleAbsci is looking for a Senior/Staff AI Scientist to advance and deploy protein design models for antibody drug design. In this role, you will apply your broad expertise across specialized disciplines in AI Drug Discovery. We are looking for exceptional contributors with backgrounds in deep learning, protein design and engineering, drug discovery, natural language processing, computer vision, and molecular dynamics to develop innovative approaches to creating and assessing therapeutic antibodies in silico.
Absci offers AI Scientists a unique opportunity to both develop novel, cutting edge machine learning models and apply these models directly to identify novel targets, unlocking previously intractable disease interventions, and to generate candidate antibody therapeutics.
This is a remote position, with the option to work onsite at our New York City office or our Vancouver, WA headquarters if within commuting distance
Key Responsibilities- Develop, adapt, and deploy deep learning models that predict the intra- and intercellular signaling effects of potential therapeutic interventions
- Design experiments which generate data to train and validate systems biology models
- Collaborate closely with cross-functional teams comprising Disease Biologists, Structural Biologists, Computational Biologists, and Wet Lab scientists to define and address the problem space within specific indications
- Develop in silico and in vitro validation approaches to iteratively improve design and evaluation methodologies
- Communicate and present experimental results in a manner that is accessible to audiences with highly-diverse backgrounds, driving discussions that lead to high-quality, informed decision-making and program progression
- Deliver and publish high-impact research that advances Absci's position as a leader in AI-guided antibody therapeutic discovery
- Actively develop organizational talent through coaching and mentoring other Scientists and Engineers
- Ability and willingness to learn new technical skills to improve scientific contributions
Qualifications- PhD or equivalent experience in Machine Learning, Computer Science, Computational Biology, Computational Chemistry, Biophysics, or a related field
- Seasoned deep learning expert with 3+ years of post-graduate experience and a strong background in several of the following areas: biological world models, mathematical biology, systems biology, disease biology, computational biology/multi-omics, experiment design to generate and/or handling intra- and intercellular perturbation datasets
- Fluency in Python and PyTorch
- Expertise in large-scale model architecture design and training
- Mastery of proper scoring rules, validation metrics for highly imbalanced biological datasets, and active learning paradigms.
- Demonstrated ability to work collaboratively in an ambitious, fast-paced, interdisciplinary environment
- Demonstrated experience presenting complex technical work to diverse audiences
- Strong publication record in respected, high-impact journals and conferences
