We are a pioneering next-generation therapeutics organization at the forefront of the convergence between computation and life sciences. Our mission is to revolutionize drug discovery by delivering safer and more effective therapeutics that defy traditional drug discovery pipelines.
The team is using generative AI to accelerate drug discovery by designing precise protein therapeutics, such as antibodies, for diseases like cancer and diabetes. Their technology combines molecular simulations, synthetic biology, and high-throughput experiments with large-scale computational infrastructure to develop patient-specific treatments efficiently and cost-effectively. The team is made up of curious, impact-driven professionals from diverse disciplines, united by a passion to redefine the future of medicine.
Responsibilities:
Collaborate closely with the founding team and advisors to develop and deploy generative learning models on AWS and GCP, utilizing frameworks like Ray and Anyscale.
Execute machine learning training and inference workloads distributed across thousands of GPUs.
Design and maintain machine learning tools and libraries to support a scalable, efficient, and flexible platform.
Establish validation and benchmarking processes to uphold high-quality standards for ML models.
Exhibit autonomy and critical thinking by taking full ownership of projects and deliverables.
Incorporate feedback from biological labs conducting extensive experiments on proteins and cancer cells to refine models and processes.
Requirements:
A degree in computer science, applied mathematics, computational biology, or a related quantitative discipline.
Over four years of hands-on experience in developing and deploying deep learning models.
Proven ability to distill complex scientific concepts, address novel and challenging problems, and work collaboratively within cross-functional teams.
Expertise in working with large-scale models on distributed computing infrastructures.
Proficiency in designing and maintaining software libraries, adhering to industry best practices and standards.
Desired Skills & Attributes:
Knowledge of generative models, reinforcement learning, and natural language processing algorithms.
Peer-reviewed publication(s) in the field of machine learning and/or experience contributing to open-source libraries.