Quantum-Si
Overview
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 and 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 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. 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
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 Salary information: The estimated base salary range for this role based in the United States is $130,000 - $155,000. Compensation decisions are based on factors including level, skills, knowledge, location, internal equity, and market data. Full-time employees are eligible for a discretionary bonus program and equity as part of the compensation package. Equal Opportunity and Compliance
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 information will be kept confidential according to EEO guidelines.
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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 and 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 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. 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
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 Salary information: The estimated base salary range for this role based in the United States is $130,000 - $155,000. Compensation decisions are based on factors including level, skills, knowledge, location, internal equity, and market data. Full-time employees are eligible for a discretionary bonus program and equity as part of the compensation package. Equal Opportunity and Compliance
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 information will be kept confidential according to EEO guidelines.
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