Quantum-Si
Senior Scientist, Computational Biology
Quantum-Si, San Diego, California, United States, 92189
Overview
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.
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 environments for data analysis, interactive modeling, and prototyping
Strong analytical thinking and practical problem-solving skills
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, with hands-on experience conducting in silico analyses
Familiarity with protein structure prediction and design using 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 depend on factors including the level, skills, location, internal equity, and market data. All full-time employees are eligible for discretionary bonus and equity as part of the compensation package.
Quantum-Si is an E-Verify and equal opportunity employer. All information will be kept confidential according to EEO guidelines.
Seniority level Mid-Senior level
Employment type Full-time
Job function Research, Analyst, and Information Technology
Referrals increase your chances of interviewing at Quantum-Si by 2x
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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.
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 environments for data analysis, interactive modeling, and prototyping
Strong analytical thinking and practical problem-solving skills
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, with hands-on experience conducting in silico analyses
Familiarity with protein structure prediction and design using 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 depend on factors including the level, skills, location, internal equity, and market data. All full-time employees are eligible for discretionary bonus and equity as part of the compensation package.
Quantum-Si is an E-Verify and equal opportunity employer. All information will be kept confidential according to EEO guidelines.
Seniority level Mid-Senior level
Employment type Full-time
Job function Research, Analyst, and Information Technology
Referrals increase your chances of interviewing at Quantum-Si by 2x
#J-18808-Ljbffr