Senior Machine Learning Scientist

**About Tahoe Therapeutics** Tahoe Therapeutics is a biotechnology company pioneering a fundamentally new approach to drug discovery, one that begins with the biology of real patients. Our Mosaic platform is the first to make in vivo data generation scalable, with single-cell resolution, allowing us to map how drugs affect patient-derived cells in the body across a wide range of biological contexts. We are building the world’s largest in vivo single-cell perturbation atlas and using it to train multimodal foundation models that learn the context-dependent nature of gene function, disease progression, and drug [](http://response.By) combining cutting-edge machine learning with the most biologically relevant datasets ever assembled in drug discovery, our mission is to find better drugs, faster and bring them to more patients who need them. **Your role** With Tahoe-100M, we solved one of the fundamental bottlenecks in building a virtual model of the cell: generating massive, perturbation-rich, single-cell datasets that capture real biological causality. With **[Tahoe-x1](https://huggingface.co/tahoebio/Tahoe-x1)**, we removed the second bottleneck: creating a modern platform for rapid iteration on model architectures and designs in a cost-efficient manner and at scale. At Tahoe, we embody a simple philosophy: build in the open, shoot for the moon, and we’re looking for people who want to push the frontier of what’s possible. As a **Senior Machine Learning Scientist**, you will play a leading role in designing the next generation of foundation models of gene regulatory networks powered by Tahoe’s large scale single-cell datasets such as **[Tahoe-100M](https://huggingface.co/datasets/tahoebio/Tahoe-100M)**and beyond. This role is well-suited for someone with a strong background in machine learning and statistics, and an interest in applying cutting-edge breakthroughs in ML to meaningful problems in drug discovery. We are looking for non-incremental thinkers with the skills to help build models that can make a real impact on drug discovery. ### Qualifications - Essential - PhD or equivalent practical experience in a technical field. - 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). - A genuine enthusiasm for applying cutting-edge ML research to real-world biological problems and a bias towards action. ### Qualifications - Nice to have - 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) ### Key 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. ### Benefits - Unlimited Paid Time Off (PTO). - Monthly Lunch budget. - One-time Office set up budget. - US Employees: HMO Kaiser Platinum and PPO Anthem Gold medical as well as vision and dental plans for both the employee and dependents.