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About Abalone Bio
Abalone Bio is a biotech company pioneering the discovery and design of functionally active antibodies, with an initial focus on G-protein coupled receptors (GPCRs). We overcome the major challenge of discovering rare GPCR agonist antibodies by using our proprietary Functional Antibody Selection Technology (FAST), a high-throughput, yeast cell-based platform.
Our drug discovery engine is fundamentally differentiated by generating millions of direct antibody sequence-to-function relationships through Next-Generation Sequencing (NGS) of functionally active clones. These robust, high-quality functional datasets are orders of magnitude larger than industry standards and serve as the foundation for our cutting-edge AI/ML-driven design capabilities. We utilize sophisticated machine learning models, including Protein Large Language Models (PLLMs) and Reinforcement Learning (RL), to both discover functional activity and design novel, optimized antibody agonists.
We are seeking a talented ML Research Consultant to help us on our mission. You will work closely with our ML team to ideate, implement, and validate the machine learning classifiers and generative models that will ultimately improve our therapeutic design platform, translating functional activity data into breakthrough biologics.
What You Will Do
Evaluate Sequence-Function Models: Design, implement, and evaluate state-of-the-art machine learning models, including Protein Large Language Models (PLLMs), to predict functional activity of GPCR agonist antibodies.
Implement Generative Models: Design, implement, and evaluate state-of-the-art generative models using Reinforcement Learning or Bayesian Optimization for de novo GPCR agonist antibody design.
Experimentation: Design and execute rigorous ML experiments with clear hypotheses and documented outcomes and next steps.
Required Qualifications
Bachelor’s or Master’s (5+ years of experience) or Ph. D. degree (2+ years of experience) in Bioinformatics, Biophysics, Computational Biology, or a related quantitative field.
Experience solving complex business problems in the life sciences using ML or statistical methods Excellent communication skills particularly in the context of cross-functional life science teams Understanding of modern deep learning architectures and optimization techniques as well as fundamentals of machine learning.
2+ years of hands-on experience developing deep learning models using frameworks like PyTorch.
Strong proficiency in python and the scientific computing stack (NumPy, Pandas, scikit-learn).
Proficiency with software engineering best practices, version control (Git), and testing frameworks.
Experience with Transformer architectures, Recurrent Neural Networks (RNNs), or Graph Neural Networks (GNNs) applied to biological sequence or molecular/structural data.
Preferred Qualifications
Theoretical foundations and practical experience with methods in computational structural biology, e.g., protein structure prediction, molecular dynamics, coarse-grained modeling, etc.
Experience with high-throughput screening data, particularly NGS analysis, flow cytometry, or other assays that link genotype to phenotype.
Direct experience with Protein Large Language Models (PLLMs), generative modeling, and/or Reinforcement Learning (RL) in a protein engineering context.