BigHat Biosciences
About the start-up
BigHat Biosciences is designing safer, more effective antibody therapies for patients using machine learning and synthetic biology.
BigHat integrates a wet lab for high-speed characterization with machine learning technologies to guide the search for better antibodies.
We are applying these design capabilities to develop new generations of safer and more effective treatments for patients suffering from today’s most challenging diseases.
BigHat operates out of San Mateo, CA, in the San Francisco Bay Area. We pride ourselves on our team-oriented, inclusive, remote-friendly, and family-centric culture.
The Opportunity
BigHat has two possible roles (only one will get a scholarship).
One project is centered around developing approaches for extending antibody half life.
The second project is centered around interpreting and modeling multi-modal data for antibody design and development.
The findings from these projects will help future BigHat molecules achieve better dosing through extended antibody half life.
Primary responsibilities
Experimental position - protein biochemist, systems biologist, or molecular biologist.
Main responsibilities will include (but are not limited to):
Design and execute experiments to optimize antibody expression, stability, activity, and binding affinity.
Evaluate and develop in vitro cell-based functional assays to assess activity of therapeutic antibody.
Work collaboratively to scale new assay types to be run at high throughput.
Summarize results and critical problems in writing and in presentation to a collaborative, cross-functional team.
Computational position - data scientist or bioinformatics
BigHat Biosciences is seeking an exceptional, self-motivated individual to lead a project to process and interpret our multi-modal data on antibody function and developability. Main responsibilities will include (but are not limited to):
Designing and testing improvements to our existing data pipelines and quantifying their impact on our ML models and antibody design
Designing and building new data pipelines that effectively combine data across multi-modal assays to improve our QC, ML models, and ultimately our antibody design
Developing and implementing innovative experimental designs and statistical modeling to quantify the effectiveness of and improve our antibody design strategies.
Close collaboration with the BigHat wet lab, software engineering, and scientific teams to translate key findings into actionable improvements for our platform.
About you
Experimental position
Field of study within Protein biochemistry or Molecular and Cellular biology (or similar)
Familiarity with protein expression and purification
Experience analyzing datasets, making figures, and summarizing experiments.
Positive attitude and willingness to learn
Computational position
Masters candidate with the following skills:
Field of study within bioengineering, bioinformatics, biophysics, computer science
>1 year of developing, evaluating and applying bioinformatics pipelines
Knowledge of and practical experience (>1 year) with classic statistical models (regression, ANOVA, random effects models), experimental design
Working knowledge of concepts and models in AI/ML (SVMs, deep learning)
Excellent communication skills, ability to translate findings to scientists AND engineers
Self-motivated, ability to work in fast-paced environment with many different data types
Pluses
Experimental position
SPR/BLI
ELISA
Molecular cloning
Cell culture
Experience with R programming.
Computational position
Prior experience with protein biochemical (e.g. binding affinity, purity, thermostability, etc.) and biophysical (e.g. structure) data
Prior experience with experimental design in a data streaming context or with active learning or Bayesian optimization
Looking for
Protein biochemist, Systems biologist, Molecular biologist, Bioengineering, Bioinformatics, Biophysics, Computer science
Location
San Mateo, California