ML Engineer @ Vevo Therapeutics

About Us

We are a biotechnology company based in San Francisco. We are using our in vivo drug discovery platform and AI models trained on its data to uncover better drugs for more patients. Our Mosaic platform is the first to make in vivo data generation scalable, with single-cell precision, to capture in vivo context of disease at the first step of drug discovery and to better represent patient diversity in drug response over current in vitro assays. We are using Mosaic to build the world’s largest in vivo atlas of how drugs interact with patient cells and how they alter gene function in diverse biological contexts. We are using the unprecedented scale and sophistication of the data in this atlas to train single-cell foundation models that learn context dependency of gene function and help us find novel targets and drugs that are more likely to work in patients.

Your Role

At Vevo, we generate a huge amount of data on how drugs interact with cells from many different patients, at a single-cell resolution and in an in-vivo context. We are using this massive perturbational atlas to develop the next generation of Foundation Models that accurately capture the biology of disease and enable the development of better therapies. We are seeking an experienced ML Engineer to help us rapidly implement and scale up these models. You will be part of our in silico team, with colleagues who are specialized in bioinformatics, computational biology and machine learning, and also with experimental scientists.

Qualifications - Required

Solid Engineering and Computer Science fundamentals, ideally with a degree in CS, Math, or equivalent experience.
Experience in building, testing, training, and deploying modern neural network architectures such as Transformers.
Experience with frameworks like PyTorch, Tensorflow, Keras, JAX, etc.
Experience and familiarity with modern software engineering practices such as VCS, Docker, CI/CD.

Qualifications - Desirable

Experience with distributed deep learning using frameworks such as HF Accelerate, Deepspeed, and Composer.
Practical experience with the development and testing of large language models.

Key Responsibilities

Support ML scientists in implementing LLM-inspired deep neural networks for single-cell transcriptomics and generative chemistry.
Design tooling for rapid and reproducible development of ML models.
Stay up-to-date on algorithmic developments to improve the efficiency of training large networks such as Flash Attention, Grouped-querry Attention etc.

Benefits

Unlimited Paid Time Off (PTO).
Manulife Silver including medical, vision and dental.

Qualifications - Required

Qualifications - Desirable

Key Responsibilities

Benefits

6 month growth

1 year growth

2 year growth