Tahoe Therapeutics is looking to solve bottlenecks in drug discovery by building a virtual model of the cell and creating a modern platform for rapid iteration on model architectures and designs. They aim to find better drugs faster by using machine learning on large-scale single-cell datasets.
Requirements
- A proven track record of developing and applying deep learning methods, including experience with modern architectures such as transformers, state-space models, graph neural networks or diffusion-based generative models.
- Proficiency with modern ML frameworks (e.g., PyTorch, JAX, or TensorFlow) and core scientific computing libraries (e.g., NumPy, SciPy, Pandas).
- Prior experience with ML applied to problems in biology or chemistry.
- Familiarity with multimodal modeling, contrastive learning or self-supervised learning.
- Experience with large scale distributed ML techniques (e.g., FSDP, TP, dMoE, flash attention).
Responsibilities
- Develop and apply machine learning techniques towards building multimodal foundation models that bridge the chemical and biological domains, i.e.: integrate models of chemical structure, target protein sequence and whole transcriptome scRNAseq.
- Stay at the forefront of ML and computational biology research and rapidly adopt state-of-the-art techniques to our problems and datasets.
- Collaborate with our team of biologists and engineers in cross-functional pods to test novel ML-driven hypotheses.
Other
- PhD or equivalent practical experience in a technical field.
- A genuine enthusiasm for applying cutting-edge ML research to real-world biological problems and a bias towards action.
- This position requires on-site presence at our South San Francisco office a minimum of three days per week.
- We welcome applicants who require visa sponsorship and provide work authorization support for qualified candidates.
- Unlimited Paid Time Off (PTO).
