Episteme

Job Summary:
Episteme is a new kind of R&D company supporting exceptional researchers in translational science. The Member of Research Staff role involves developing machine learning models to decode and model neural dynamics, working closely with experimental and computational scientists.
Responsibilities:
• Develop and test ML models for processing and integrating multi-modal data priors (e.g., calcium imaging, connectomics, and voltage dynamics of individual neurons and circuits)
• Develop and test ML models for predicting neural activity and behavior from experimental datasets
• Explore advanced deep learning methods (e.g., symbolic regression, GNNs, Transformers, generative models) to inform neural models from data and enhance interpretability
• Apply statistical and causal inference methods to identify candidate circuit mechanisms
• Develop models and software that can be broadly adopted by the scientific community through open-source or commercial distribution models.
• Contribute to the collaborative, interdisciplinary environment of the project
• Follow a structured research plan, with defined tasks and milestones.
Qualifications:
Required:
• Ph.D. in Computer Science, Applied Mathematics, Computational Neuroscience, Machine Learning, or a related field
• Strong track record developing machine learning methods for multidimensional signal, image, or sequence data
• Experience building models across predictive, generative, classification, or regression tasks
• Demonstrated expertise in statistical modeling, predictive analysis, and causal inference
• Experience developing impactful computational tools, models, or software systems
• Proficiency with Python and modern machine learning frameworks such as PyTorch or JAX
• Strong scientific communication and collaborative software development practices
Company:
Episteme conducts cross-disciplinary research and development focused on translational science. Founded in , the company is headquartered in San Francisco, USA, with a team of 11-50 employees. The company is currently Early Stage.