This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more.

Role Description

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.

• 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.

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.

Benefits

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