The Role: We are seeking a self-motivated, accomplished Machine Learning Scientist* to advance the state of the art in ML-driven antibody design. At BigHat Biosciences we frame antibody drug development as an iterative multi-objective optimization problem with a wet lab in-the-loop. A weekly build-test-train cycle lets us quickly evaluate our latest generative and predictive models on real-world therapeutics design challenges, and active learning and bayesian optimization methods let us rapidly and intelligently gather unprecedented volumes of training data to continuously improve these models. As an ML Scientist excited about developing treatments for unmet patient need, you’re not just searching through biology for applications that fit the latest developments in machine learning. Rather, you’re using a deep understanding of real world drug development problems (arrived at with your interdisciplinary colleagues) to inform the development of the next generation of ML-driven protein engineering tools. *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.
Key Responsibilities
Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies.
Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for deployment and validation in our high-throughput wet lab - we only trust algorithms after they’ve designed real antibodies with drug-like properties.
Provide ML expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs.
Collaborate with our engineering team to ensure maximal efficiency in the automated deployment of our latest models to ongoing drug development programs.
Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. to identify inefficiencies or potential improvements in the BigHat ML platform, and plan the next phase of ML research accordingly.
Preferred Qualifications
PhD in Machine Learning or equivalent experience developing and applying novel ML methods.
Publications in major ML conferences and/or leading journals, or extensive demonstrable track record developing and applying novel ML in industry.
Strong competency in Python, familiarity with pytorch.
Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
Enjoys a fast paced environment and executing across multiple projects.
Nice-to-haves include experience with protein structure modeling and biophysics, NGS data, and familiarity with antibody biology & biophysics.
