- Building and training of sophisticated transformers operating on molecular graphs.
- Processing of datasets to feed into these models.
- Application of these models to different drug discovery tasks.
- Surveying and keeping up-to-date with the relevant literature to see if it can be integrated into our projects.
- BS/MS in computer science, mathematics, physics, chemistry or related field from a top institution.
- 1-3+ years of experience in a MLE role at a tech or biotech company.
- Experience training transformer architectures.
- Experience training deep nets on multiple GPUs.
- Excellent interpersonal skills, oral and written communication skills. Willing to genuinely work within an interdisciplinary team and learn the domains of other fields.
- Comfortable in Pytorch and/or JAX.
- PhD in machine learning, chemistry, structural biology or related field.
- Previously applied machine learning to chemistry or structural biology.
- Experience with bio and chem toolkits (e.g RDKit, Openeye, BioPython, etc)
- Savvy in state-of-the-art deep learning techniques (gradient rematerialization etc).
- ML Ops tools (Pytorch Lightning, onnx, jit, deepspeed).
- Understanding of equivariance and representation theory.