Principal Scientist, Antibody Engineering & Rational Design at Calico

South San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

20 Mar, 26

Salary

241000.0

Posted On

20 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Molecular Biology, Biochemistry, Computational Modeling, Structural Biology, Antibody Engineering, Machine Learning, Data-Driven Approach, Experimental Validation, Structural Modeling, Protein Design, High-Quality Training Data, Antibody Structure-Function Relationships, Rational Design, Biophysical Characterization, CRO Management, Novel Scientific Approaches

Industry

Biotechnology Research

Description

Who We Are: Calico (Calico Life Sciences LLC) is an Alphabet-founded research and development company whose mission is to harness advanced technologies and model systems to increase our understanding of the biology that controls human aging. Calico will use that knowledge to devise interventions that enable people to lead longer and healthier lives. Calico’s highly innovative technology labs, its commitment to curiosity-driven discovery science, and, with academic and industry partners, its vibrant drug-development pipeline, together create an inspiring and exciting place to catalyze and enable medical breakthroughs. Position Description: We are seeking an innovative Principal Scientist to bridge the gap between computational design and experimental validation in our antibody discovery engine. In this role, you will serve as the primary scientific liaison between our wet-lab antibody team and our machine learning team, driving the evolution of our "Design-Build-Test-Learn" cycle. Acting as both a project leader and technical expert, you will translate complex in silico designs into actionable experimental workflows. You will be responsible for ensuring that computational predictions are rigorously validated and that the resulting biological data is structured to actively retrain and refine our internal models. We are looking for a deep expert in therapeutic antibody development who is eager to move beyond traditional random screening and champion a rational, data-driven approach to biologic drug discovery. Position Requirements: Ph.D. in molecular biology, biochemistry, computational modeling, structural biology, or related discipline 8+ years’ post-PhD experience in antibody/protein engineering within the biotech or biopharma sectors Demonstrated ability to work intimately with data scientists and ML engineers and able to articulate biological constraints (e.g., immunogenicity, manufacturability) to computational teams and interpret their in silico outputs for experimental validation Hands-on proficiency with structural modeling and extensive knowledge in ML protein design tools Experience designing experiments specifically to generate high-quality training data for ML models (e.g., focused libraries, systematic mutagenesis) is highly desirable Expert knowledge of antibody structure-function relationships Extensive experience in rational design for humanization, affinity maturation, pH-dependence, and liability removal Deep expertise in engineering multi-specific antibodies Strong understanding of biophysical characterization to ensure designed molecules are developable therapeutics Experience technically managing CROs for gene synthesis, protein production, and functional assays Ability to propose novel scientific approaches and solve complex technical hurdles independently Must be willing to work onsite 5 days a week The estimated base salary range for this role is $198,000 - $241,000. Actual pay will be based on a number of factors including experience and qualifications. This position is also eligible for two annual cash bonuses.

Responsibilities

The Principal Scientist will bridge the gap between computational design and experimental validation in antibody discovery. They will serve as the primary scientific liaison between the wet-lab antibody team and the machine learning team, ensuring rigorous validation of computational predictions.