Degree in Biomedical Engineering
Active Learning algorithms, Bayesian networks, Biomedical Imaging, CNNs, Deep Learning, Effective communication skills, GANs, Python Programming, R Programming, Regression Analysis, RNA-seq Data processing, Statistical modeling
The role: At BigHat Biosciences our machine learning stack is tightly integrated with a high-throughput wetlab to rapidly design and validate ML-engineered antibodies. The Machine Learning Scientist* will work to advance the state of the art at each step in this integrated, iterative antibody optimization platform, improving the effectiveness with which it can be used to design new therapeutics to address unmet patient need.
*At BigHat we believe in titles that commensurate with skill set, relative organizational impact, and value contribution; more experienced candidates are encouraged to apply, with the understanding that responsibilities and title would adjust as appropriate.
Job Responsibilities
Preferred Qualifications
About BigHat Biosciences
BigHat Biosciences designs safer, more effective biologic 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 apply these design capabilities to develop new generations of safer and more effective treatments for patients suffering from today’s most challenging diseases.
BigHat is a Series B biotech outside San Francisco with a team-oriented, inclusive, and family-friendly culture. Our broad pipeline of wholly-owned and partnered therapeutic programs span many disparate indications with high unmet need, such as cancer, inflammation, and infectious disease. BigHat has raised >$100M from top investors, including Section 32, a16z, and 8VC.
BigHat’s mission is to improve human health by making it far easier to design advanced, next-generation antibody therapeutics. Our AI-enabled experimental platform integrates a high-speed characterization or “wet” lab with machine learning technologies to speed the antibody engineering process. When applied, these design capabilities have the potential to drive the development of new generations of safer and more effective treatments for patient
San Mateo, CA, USA
4-6 year