Founding Scientist — Generative & Causal ML for Healthcare

Compensation: 1.0% – 3.0%

About the Company

Neura Augma (Neau AI) is an autonomous intelligence research and product company focused on making autonomous intelligence as trusted as the healthcare experts it serves. Our product, HALE, is an execution layer for healthcare AI designed to solve one of the field’s foundational challenges: the lack of a reliable health data foundation for practical, scalable, autonomous AI.

Role Overview

This founding member will own the core AI/ML systems powering our synthetic intelligence capabilities, designing and building models that generate realistic, high-fidelity scenarios for complex real-world environments. The role blends applied machine learning, data systems thinking, and product intuition, with responsibility for turning new ideas into robust, production-ready features. This is an equity-forward, founder-type role with the potential to grow into leadership, with flexibility for part-time or full-time engagement depending on fit and stage.

Academic researchers and postdocs with relevant ML, healthcare AI, or data systems experience are welcome to apply.

Key Responsibilities

• Design models for structured, temporal, multimodal, and causal data
• Build, train, and evaluate ML architectures and models
• Support distributed or federated learning across multi-site or cross-boundary settings
• Develop privacy-preserving training and evaluation methods
• Establish reproducibility, versioning, and testing standards
• Optimize models for constrained environments (limited compute, memory, etc.)
• Help shape product direction through technical decisions

Ideal Background

• Strong experience in generative modeling, data fusion, and multimodal models
• Comfort with real world datasets and irregular time series
• Experience with EHR standards such as OMOP, FHIR, or similar frameworks
• Familiarity with medical ontologies (SNOMED, LOINC, RxNorm) or knowledge graphs
• Interest in learning hybrid classical-quantum methods (Qiskit, PennyLane)
• Ability to translate research concepts into production grade code
• Entrepreneurial mindset: thrives in ambiguity, builds fast, iterates fast

What Success Looks Like

You prototype and deliver ML architectures for realistic, high‑fidelity data modeling and privacy preservation, bridging research and production while supporting scientific collaboration.