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PhysicsX is a deep-tech company building AI-driven simulation software for engineering and manufacturing across aerospace, defense, materials, energy, semiconductors, and automotive. The Principal Research Scientist will own high-level research work-streams, setting technical direction and delivering outcomes that unlock new optimization and automation in design, manufacturing, and operations.
Key responsibilities include: owning research work-streams end-to-end, aligning priorities with internal and external stakeholders, setting technical direction, planning roadmaps with clear milestones, organizing and guiding junior team members, and communicating outcomes across the company. You will contribute to research group strategy and culture by identifying valuable research areas, championing their development, promoting effective working patterns, and nurturing younger colleagues' professional growth.
Specific technical work involves translating physics and engineering challenges into mathematical formulations, building models to predict physical system behavior using state-of-the-art machine learning and deep learning, collaborating with ML engineers and simulation engineers, and translating models into production-ready code. You will communicate work through academic and non-academic channels including paper publications, industry workshops, and customer conversations.
Required qualifications: PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or related field with expertise in operator learning/neural operators for PDEs, geometric deep learning for 3D data, or generative models for geometry and spatiotemporal data. Ideally 4+ years in professional industry data-driven roles building ML models and pipelines in Python (PyTorch, JAX, NumPy/SciPy), developing models for high-dimensional data, iterating on architectures, combining theory with empirical intuition, and formulating experiment pipelines. Publication record in reputable venues (NeurIPS, ICML, ICLR, CVPR, etc.) demonstrating mastery in relevant domains is expected.
