Scientist/Developer, Machine Learning Force Field Development
New Equilibrium Biosciences discovers drugs that target intrinsically disordered proteins through its integrated computational-experimental platform, with the mission of creating transformative medicines for patients with cancers and neurodegenerative disorders. We are seeking MS- or PhD-level researchers and developers with strong backgrounds and interest in machine learning, data engineering, computational chemistry, or cloud development to join our interdisciplinary team starting in Summer 2021. Candidates should be excited about building interdisciplinary computational frameworks to guide drug discovery and revolutionize pharmaceutical development.
- M.S. or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Computational Chemistry, Computational Biology, Electrical Engineering, or a related field; industry or postdoc experience a plus
- Positive attitude
- Attention to detail and enthusiasm for benchmarking
We are recruiting multiple researchers/developers to work on our force field development pipeline, where the initial focus of each new team member will be in one or more of the following areas, depending on their skills and interests:
- Dataset generation: Expand our high quality quantum chemistry data set. Focus on algorithms and pipelines for creating a diverse dataset in a computationally efficient manner.
- AI force field development: Expand our machine learning potential for describing protein motion. Implement and test state of the art algorithms with a focus on accuracy, scalability, and transferability (requires PyTorch).
- Profiling and optimization: Improve computational efficiency of implemented algorithms, data loaders, and training procedures. Design hyper-parameter optimization procedures and improve scalability with system size and CUDA cores.
- Software interfacing: Ensure that designed algorithms can be interfaced with molecular dynamics software packages in a computational efficient manner (requires C++ and CUDA).
- Cloud and deployment: Research, advise, and implement best practices for making trained models available to our computational biologists in a persistent and systematic manner. Improve accessibility and cost efficiency of our cloud use, including cloud-agnostic implementations.
- Next-gen hardware: Explore the use of next-gen hardware (dedicated chips, quantum computing, etc.) to expand the size and timescales of biological systems studied at New Equilibrium from both a hardware and software perspective.
If interested, please apply via our google form by July 5th, 2021. Reach out to firstname.lastname@example.org if you have any questions. We look forward to hearing from you!