As a Senior Machine Learning Scientist, you will develop multimodal foundation models using large-scale single-cell datasets to advance drug discovery, applying deep learning methods and collaborating with biologists and engineers.
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 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, 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 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.
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.
Tahoe Therapeutics South San Francisco, California, USA Office
681 Gateway Blvd, South San Francisco, CA , United States, 94080
Similar Jobs
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
Lead the development and production deployment of machine learning, generative AI, and multi-agent solutions. Translate business problems into scalable AI products, build models and data pipelines, architect cloud and lakehouse systems, and implement MLOps, LLMOps, evaluation, governance, security, and monitoring. Partner with cross-functional stakeholders, communicate technical outcomes, troubleshoot production issues, mentor teammates, and shape the strategic roadmap for enterprise AI capabilities across Azure, Databricks, AWS, and GCP.
Top Skills:
Agent OrchestrationAmazon BedrockAmazon EksAmazon EmrAmazon S3SparkAws GlueAws LambdaAws SagemakerAzure Ai FoundryAzure Data FactoryAzure Data Lake Storage Gen2Azure DatabricksAzure DevopsAzure Event HubsAzure FunctionsAzure Key VaultAzure Kubernetes ServiceAzure Machine LearningAzure MonitorAzure OpenaiBigQueryCi/CdCloud RunDatabricks LakehouseDatadogDataflowDelta LakeDockerEmbeddingsFastapiFivetranFoundation ModelsFunction CallingGeminiGitGitGithub ActionsGoogle Cloud StorageGoogle Kubernetes EngineGoogle Vertex AiHelmInfrastructure As CodeJupyterKubernetesLarge Language ModelsLightgbmLlmopsMlflowMlopsMosaic AiNumpyOpenapiOpentelemetryPandasPower BIPrompt EngineeringPub/SubPysparkPythonPyTorchRest ApisRetrieval-Augmented GenerationScikit-LearnScipySQLTensorFlowTerraformUnity CatalogVector DatabasesXgboost
Artificial Intelligence • Software
Leads computational protein engineering initiatives using generative and predictive machine learning to design therapeutic biomolecules. Owns workflows from design specification through wet-lab validation and active learning, develops sequence- and structure-based models, builds evaluation frameworks, and integrates protein design methods into autonomous science software. Collaborates with experimental scientists, AI researchers, and platform teams to interpret results and improve biomolecular design strategies.
Top Skills:
Active LearningDiffusion ModelsFlow MatchingGenerative AiMachine LearningPredictive ModelingProtein Language ModelsStructure Prediction
Cloud • Computer Vision • Information Technology • Sales • Security • Cybersecurity
Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.
Top Skills:
Cloud TechnologiesDeep Learning FrameworksGenerative AiGpu TechnologiesLlmsPython
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine



