Senior Scientist, Computational Biology

We are seeking a highly motivated and experienced Senior Scientist with expertise in computational biology and machine learning to join the Data Science & Algorithms Team. This role focuses on designing and optimizing protein binders with high affinity for N-terminal amino acid targets, a critical component of our Next-Generation Protein Sequencing kit.  

You will work at the intersection of machine learning, protein engineering, and structural biology, leveraging state-of-the-art algorithms and experimental feedback to develop novel protein scaffolds with tailored binding characteristics.  

The ideal candidate will have a deep background in Computational Biology, Bioinformatics, Data Science, or a related field with 5+ years of relevant academic or industry experience.  The candidate also must have a strong knowledge of programming languages (e.g. Python, Bash) and experience with developing or fine-tuning machine learning models. Candidates with a demonstrated ability to apply machine learning to protein design, structure-function prediction, or generative modeling are especially encouraged to apply. Familiarity with state-of-the-art protein modeling software (e.g. AlphaFold, ProteinMPNN) is a plus. 

 

As part of our team, your core responsibilities will be:   

• Design, model, and computationally screen protein binders for selective binding to N-terminal amino acid motifs. 

• Develop and optimize binder scaffolds using a combination of structure-based design, ML-driven design, and generative protein modeling tools. 

• Collaborate with wet-lab teams to iteratively test, validate, and refine designs using experimental feedback. 

• Innovate new computational pipelines for high-throughput protein binder discovery. 

• Evaluate binding energetics, specificity, and structural feasibility using in silico approaches. 

 

Qualifications  

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, Data Science, or a related computational/scientific field 

• Skilled in ML model development and/or fine-tuning, especially for protein structure-function prediction and generative protein design 

• Experience integrating experimental feedback loops into computational pipelines to improve design success 

• Experience developing custom computational methods or ML approaches to guide protein design toward desired structural/functional properties 

• Proficient in programming with Python (preferred) and/or other scripting languages such as Bash; familiarity with JupyterLab, Jupyter Notebooks, or similar virtual notebook environments for data analysis, interactive modeling, and prototyping. 

• Strong analytical thinking and practical problem-solving skills, including the ability to break problems into logical subproblems and devise efficient and flexible solutions 

• Excellent scientific communication and documentation skills, including data summarization and visualization using Python 

Ideally, you also have these skills/experiences/attributes (but it’s ok if you don’t!): 

• Strong understanding of protein-protein and protein-peptide interactions, as well as hands-on experience conducting in silico analyses to evaluate these interactions 

• Familiarity with protein structure prediction and design using cutting-edge modeling software (AlphaFold, ProteinMPNN, RFDiffusion, ESM, Rosetta, etc.) 

• Experience designing binders against unstructured peptide regions, including terminal epi