Senior Scientist, Computational Biology

Quantum-Si; San Diego (CA); On-site; USD 130,000 - 155,000; Full time

Job summary

A leading company in innovative protein technologies is seeking a Senior Scientist specializing in computational biology to advance its protein sequencing initiatives. The role involves designing, modeling, and optimizing protein binders using state-of-the-art methodologies in a collaborative environment, perfect for those looking to leverage their expertise in a cutting-edge field.

Benefits

Discretionary bonus program

Equity opportunities

Qualifications

• 5+ years of experience in a relevant field (academic or industry).
• Proficient in programming with Python and Bash.
• Experience with protein modeling software.

Responsibilities

• Design and optimize protein binders for N-terminal amino acids.
• Collaborate with lab teams to validate computational designs.
• Innovate computational pipelines for protein discovery.

Skills

Computational Biology

Protein Structural Modeling

Machine Learning

Analytical Thinking

Problem-Solving

Scientific Communication

Data Visualization

Education

PhD in Computational Biology or related field

Tools

AlphaFold

ProteinMPNN

Python

Bash

JupyterLab

Job description

We are seeking a highly motivated and experienced Senior Scientist with expertise in computational biology, protein structural modeling and engineering, 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 protein engineering, machine learning, and structural biology, leveraging state-of-the-art algorithms experimental feedback to develop novel protein scaffolds with tailored binding characteristics.

The ideal candidate will have a deep background in Computational Biology, Bioengineering, Structural Biology, 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 protein modeling software (e.g. AlphaFold, ProteinMPNN). Knowledge of fine-tuning or generating new machine-learning models 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

• PhD. in Computational Biology, Bioengineering, Structural Biology, or a related computational/scientific field
• Proven expertise in protein structure prediction and design using cutting-edge modeling software (AlphaFold, ProteinMPNN, RFDiffusion, ESM, Rosetta, etc.)
• Strong understanding of protein-protein and protein-peptide interactions, as well as hands-on experience conducting in silico analyses to evaluate these interactions
• Experience developing custom computational methods or ML approaches to guide 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!):

• Publications showcasing the use of advanced protein design tools (e.g., AlphaFold, RFDiffusion, ProteinMPNN) to develop proteins with targeted functions or novel structural features
• Experience designing binders against unstructured peptide regions, including terminal epitopes or motifs
• Experience with ML model development or fine-tuning, particularly for structure-function prediction or generative protein design.
• Experience integrating experimental feedback loops into computational pipelines to improve design success.
• Familiarity with GPU-accelerated computing and scaling workflows using HPC or cloud resources.
• Experience with Git

The estimated base salary range for this role based in the United States of America is: $130,000 - $155,000. Compensation decisions are dependent on several factors including, but not limited to, level of the position, an individual’s skills, knowledge and abilities, location where the role is to be performed, internal equity, and alignment with market data. Additionally, all full-time employees are eligible for our discretionary bonus program and equity as part of the compensation package.

Quantum-Si does not accept agency resumes.

Quantum-Si is an E-Verify and equal opportunity employer regardless of race, color, ancestry, religion, gender, national origin, sexual orientation, age, citizenship, marital status, disability or Veteran status. All your information will be kept confidential according to EEO guidelines.