ML Research Consultant (AB2026-101)

Abalone Bio, Inc.; United States; Hybrid; USD 172,000 - 344,000; Full time

Job summary

Abalone Bio, Inc. is seeking a talented ML Research Consultant to advance our GPCR antibody design platform. You will collaborate with the ML team to ideate, implement, and validate classifiers and generative models that translate functional activity data into next-generation biologics.

The role emphasizes rigorous ML experimentation, deep learning methods, and communication across life sciences teams. Compensation is hourly, with part-time commitment up to 10 hours/week.

Qualifications

• Bachelor’s or Master’s (5+ years) or Ph.D. in Bioinformatics, Biophysics, Computational Biology, or related field.
• Experience solving complex life sciences problems with ML or statistics.
• Strong communication in cross-functional teams.
• Understanding modern deep learning architectures and optimization fundamentals.
• 2+ years hands-on DL model development with PyTorch.
• Proficiency in Python and NumPy/Pandas/scikit-learn.
• Experience with Git and testing frameworks.
• Experience with Transformers, RNNs, or GNNs in bio/sequence data.

Responsibilities

• Evaluate sequence-function models using state-of-the-art ML to predict GPCR antibody activity.
• Implement generative models with RL or Bayesian optimization for de novo antibody design.
• Design and run rigorous ML experiments with clear hypotheses and documented outcomes.

Skills

PLLMs

Generative models

Reinforcement Learning

Bayesian Optimization

Experiment design

Python

NumPy

Pandas

scikit-learn

Git

Transformers

GNNs

RNNs

Education

Advanced degree in Bioinformatics / Computational Biology or related

Tools

PyTorch

Jupyter

Job description

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.
• Background in developing models predicting antibody developability characteristics (e.g., stability, aggregation).
• Background in molecular/cell biology, antibody discovery, bioinformatics.

Rate: 125-250 per hour depending on experience, up to 10 hours / week