Quantum-Si
Senior Scientist, Computational Biology
Quantum-Si, San Diego, California, United States, 92189
Responsibilities
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.
Desired Skills and Experience
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 epitopes or motifs.
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.
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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.
Desired Skills and Experience
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 epitopes or motifs.
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.
#J-18808-Ljbffr